Knowledge Discovery to Support WTI Crude Oil Price Risk Management

The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as "Greeks”). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management.

The COVID-19 pandemic and global food security: a bibliometric analysis and future research direction

PurposeThis study aims to analyse the nature and trends in the knowledge discovery process on COVID-19 and food insecurity using a comprehensive bibliometric analysis based on the indexing literature in the Scopus database.Design/methodology/approachData were extracted from Scopus using the keywords COVID-19 and food security to ensure extensive coverage. A total of 840 research papers on COVID-19 and food security were analysed using VOSviewer and RStudio software.FindingsThe findings of the bibliometric analysis in terms of mapping of scientific research across countries and co-occurrence of research keywords provide the trends in research focus and future directions for food insecurity research during times of uncertainty. Based on this analysis, the focus of scientific research has been categorised as COVID-19 and food supply resilience, COVID-19 and food security, COVID-19 and public health, COVID-19 and nutrition, COVID-19 and mental health and depression, COVID-19 and migration and COVID-19 and social distancing. A thematic map was created to identify future research on COVID-19 and food security.Practical implicationsThis analysis identifies potential research areas such as food supply and production, nutrition and health that may help set future research agendas and devise policy supports for better managing food insecurity during uncertainty.Originality/valueThis analysis provides epistemological underpinnings for knowledge generation and acquisition on COVID-19 and food insecurity.

Supply chain strategy during the COVID-19 terms: sentiment analysis and knowledge discovery through text mining

The coronavirus pandemic has affected not only health but also the economy. The use of big data in finding information can be used to gain profits that logistics companies can utilize to survive during the pandemic. This study conducted text-mining research on service consultant sites in the logistics sector. This study aims to present frequency diagrams, analyze sentiment using the National Research Council (NRC) lexicon, present bigrams, and seek knowledge about strategies to minimize shipping costs and maintain inventories of manufactured goods. The words "supply", "chain", and "COVID-19" are words that are used frequently throughout the article. The results of this study showed that the words that often appear from word excavation are the words "supply", "chain", "logistics", "kpis," and "inventory". Then emotion trust becomes an emotional word that often appears in articles. The words "Supply" and "pandemic" are the words that seem the most positive and negative words, respectively. The words "COVID-19", "safety stock", and "inventory management" are words that often appear together. The result of discovery knowledge is that logistics consultants offer emotions of trust and provide many insights on minimizing shipping costs and maintaining inventory during a pandemic. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine. © 2022 IEEE.

A Big Data-Driven Intelligent Knowledge Discovery Method for Epidemic Spreading Paths

The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data. [ FROM AUTHOR]

Visualization and Visual Knowledge Discovery from Big Uncertain Data

In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.

What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey

Big Data has changed how enterprises and people manage knowledge and make decisions. However, when talking about Big Data, so many times there are different definitions about what it is and what it is used for, as there are many interpretations and disagreements. For these reasons, we have reviewed the literature to compile and provide a possible solution to the existing discrepancies between the terms Data Analysis, Data Mining, Knowledge Discovery in Databases, and Big Data. In addition, we have gathered the patterns used in Data Mining, the different phases of Knowledge Discovery in Databases, and some definitions of Big Data according to some important companies and organisations. Moreover, Big Data has challenges that sometimes are the same as its own characteristics. These characteristics are known as the Vs. Nonetheless, depending on the author, these Vs can be more or less, from 3 to 5, or even 7. Furthermore, the 4Vs or 5Vs are not the same every time. Therefore, in this survey, we reviewed the literature to explain how many Vs have been detected and explained according to different existing problems. In addition, we detected 7Vs, three of which had subtypes.

The COVID-19 pandemic and global food security: a bibliometric analysis and future research direction

Purpose - This study aims to analyse the nature and trends in the knowledge discovery process on COVID-19 and food insecurity using a comprehensive bibliometric analysis based on the indexing literature in the Scopus database.

Educational data mining in the academic setting: employing the data produced by blended learning to ameliorate the learning process

PurposeThe purpose of this paper is to introduce an interactive system that relies on the educational data generated from the online Universities services to assess, correct and ameliorate the learning process for both students and faculty.Design/methodology/approachIn the presented research, data from the online services, provided by a Greek University, prior, during and after the COVID-19 outbreak, are analyzed and utilized in order to ameliorate the offered learning process and provide better quality services to the students. Moreover, according to the learning paths, their presence online and their participation in the services of the University, insights can be derived for their performance, so as to better support and assist them.FindingsThe system can deduce the future learning progression of each student, according to the past and the current performance. As a direct consequence, the exploitation of the data can provide a road map for the strategic planning of universities, can indicate how the learning process can be updated and amended, both online and in person, as well as make the learning experience more essential, effective and efficient for the students and aiding the professors to provide a more meaningful and to-the-point learning experience.Originality/valueNowadays, educational activities in academia are strongly supported by online services, information systems and online educational materials. The learning design in the academic setting is primarily facilitated in the University premises. However, the exploitation of the contemporary technologies and supporting materials that are available online can enrich and transform the educational process and its results.

Analyse des patients hospitalises pour COVID-19 lors du premier confinement de 2020 a l'aide de methodes d'explicabilite

Figures [Formula presented] Fig. 1. Effets univaries de l'age, Severite Scanner TDM, CRP et Saturation O2. Chaque point represente un patient, avec la valeur de la variable explicative en abscisse et l'influence associee en ordonnee. [Formula presented] Fig 2. Influences des patients correspondant aux patients les plus representatifs des trois groupes identifies. Les noms des variables sont raccourcis pour les groupes 2 et 3. Les valeurs initiales des variables sont indiquees apres le trait d'union et arrondies afin qu'elles apparaissent toutes comme des nombres entiers. References 1. Institut Pasteur: Projection 'a court terme des besoins hospitaliers pour les patients COVID-19;. 2. Chen T,et al. A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining;2016. p. 785-794. 3. Bottino F, et al. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. Journal of personalized medicine. 2021;11(9):893. 4. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems;2017. p. 4768-4777. 5. Dera JD. Risk stratification: A two-step process for identifying your sickest patients. Family practice management. 2019;26(3):21-26. 6. Gestions Hospitalieres: Naviguer dans la tempete, ndegree 605 - April 2021;. Copyright © 2022

Evolution du profil des patients hospitalises au cours des trois premieres vagues de COVID-19 par des techniques d'apprentissage automatique

References 1. Williamson EJ, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature.2020;584(7821):430-436. 2. Domingo P,et al. Not all COVID-19 pandemic waves are alike. Clinical Microbiology and Infection. 2021;27(7):1040-e7. 3. Jassat W, et al. Difference in mortality among individuals admitted to hospital with COVID-19 during the first and second waves in South Africa: a cohort study. The Lancet Global Health. 2021;9(9):e1216-e1225. 4. Chen T, et al. A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining;2016. p. 785-794. 5. Lundberg SM, et al. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:180203888. 2018;. 6. Bubar KM, et al. Model-informed COVID-19 vaccine prioritization strategies by age and serostatus. Science. 2021;371(6532):916-921.March Copyright © 2022

Leveraging Free-Text Information to Detect Duplicates in COVID-19 Vaccine Adverse Event Reports: An International Journal of Medical Toxicology and Drug Experience

Introduction: Uppsala Monitoring Centre (UMC) manage VigiBase;the largest global database of reports of suspected adverse events (side effects) to medicines, on behalf of the World Health organisation (WHO). Following the emergency rollout of the vaccines against COVID-19, combined with a global focus on monitoring their safety, UMC saw a sharp increase in the volume of reports of suspected side effects of the vaccines. UMC sometimes receives multiple reports corresponding to the same suspected adverse event. This can have undesirable effects when it comes to both statistical signal detection and manual review of cases. Duplicate detection of vaccines has historically been especially challenging, due to homogeneity of patients. However, the extreme quantity of COVID-19 vaccine reports has highlighted the necessity for automated duplicate detection to be performant for them. Detecting duplicate reports is a non-trivial problem. Since reports do not always contain the same level of detail, and data errors can lead to different values in corresponding fields for duplicate reports, reports cannot simply be compared field by field. Several methods have been proposed for detecting duplicates based on information provided in structured form (sex, age, date of onset etc) (1,2). In our study we additionally incorporate free text information into a duplicate detection model. Objective: To leverage the free text information in suspected adverse event reports to identify duplicate reports which are referring to the same adverse event. Methods: Our method ensembles state-of-the-art machine learning methods.Narratives are placed in a spacewhere a smaller distance between two narratives conveys higher semantic similarity. This is done with vector embeddings using the SapBERT model, fine-tuned on a set of known duplicate reports (3). Two reports are then compared using the cosine similarity between the vector embeddings for the two narratives. This similarity is combined with representations of the structured information used in othermethods in a gradient boosted decision tree model, calibrated by a logistic regression model to fine tune the probability output (4). These methods are evaluated on a set of curated datasets of COVID- 19 vaccine reports comprising 1239 pairs of known duplicates. We use random pairs of COVID-19 vaccine reports as examples of nonduplicates. Results: Our model successfully identifies 78.9% of known duplicate pairs. It achieved a false positive rate (the number of non-duplicates erroneously marked as duplicates) of 0.001%. The full results can be seen in table 1. Conclusion: Not Applicable.

epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Similar to previous iterations, the epiDAMIK@KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://epidamik.github.io/. © 2022 Owner/Author.

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.

Comparative analysis of forecasting models in healthcare (COVID-19)

Knowledge discovery in databases (KDD) is another name of Data mining. It is an interdisciplinary area which focuses on extraction of useful knowledge from data in every sector like health, education, business etc. There are many fields to explore like business, health care, e-commerce etc but nowadays, as covid pandemic is affecting everyone and due to surge in coronavirus cases causing shortage of hospital beds, oxygen supplies, vaccine and turning away patients from hospitals, put creaky health infrastructure in spotlight. The plenty of data is available in the medical field of these conditions. To analyse the problems, there are many data mining approaches which can be used to extract useful patterns from these types of data to follow the upcoming trends. This study is to compare the various models like KNN, improved RF model and multilayer perceptron by using SPSS and python software. The data of COVID-19 has been taken from Kaggle's website which is based on the symptoms and the forecasted results has been shown. In results and conclusion, the performance of every model has shown along with this, it also shows the models and mathematical algorithms in various fields of healthcare accordingly which can be used and benefitted in medical industries. © 2022 by the Author(s).

From base data to knowledge discovery – A life cycle approach – Using multilayer networks

Analysis of complex data sets to infer/discover meaningful information/knowledge involves (after data collection and cleaning): (i) Modeling the data – an approach for deriving a suitable representation of data for analysis, (ii) translating analysis objectives into computations on the generated model instance;these computations can be as simple as a query or a complex computation (e.g., community detection over multiple layers), (iii) computation of expressions generated – considering efficiency and scalability, and (iv) drill-down of results to understand them clearly. Beyond this, it is also useful to visualize results for easier understanding. Covid-19 visualization dashboard presented in this paper is an example of this. This paper covers the above steps of data analysis life cycle using a representation (or model) that is gaining importance. With complex data sets containing multiple entity types and relationships, an appropriate model to represent the data is important. For these data sets, we first establish the advantages of Multilayer Networks (or MLNs) as a data model. Then we use an entity-relationship based approach to convert the data set into MLNs for a precise representation of the data set. After that, we outline how expected analysis objectives can be translated using keyword-mapping to aggregate analysis expressions. Finally, we demonstrate, through a set of example data sets and objectives, how the expressions corresponding to objectives are evaluated using an efficient decoupling-based approach. Results are further drilled down to obtain actionable knowledge from the data set. Using the widely popular Enhanced Entity Relationship (EER) approach for requirements representation, we demonstrate how to generate EER diagrams for data sets and further generate, algorithmically, MLNs as well as Relational schema for analysis and drill down, respectively. Using communities and centrality for aggregate analysis, we demonstrate the flexibility of the chosen model to support diverse set of objectives. We also show that compared to current analysis approaches, a “decoupling-based” approach using MLNs is more appropriate as it preserves structure as well as semantics of the results and is very efficient. For this computation, we need to derive expressions for each analysis objective using the MLN model. We provide guidelines to translate English queries into analysis expressions based on keywords. Finally, we use several data sets to establish the effectiveness of modeling using MLNs and their analysis using the decoupling approach that has been proposed recently. For coverage, we use different types of MLNs for modeling, and community and centrality computations for analysis. The data sets used are from US commercial airlines, IMDb (a large international movie data set), the familiar DBLP (or bibliography database), and the Covid-19 data set. Our experimental analyses using the identified steps validate modeling, breadth of objectives that can be computed, and overall versatility of the life cycle approach. Correctness of results is verified, where possible, using independently available ground truth. Furthermore, we demonstrate drill-down that is afforded by this approach (due to structure and semantics preservation) for a better understanding and visualization of results.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.

BACKGROUND: Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses. METHOD: We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted. RESULTS: We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org . Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework. CONCLUSION: In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Hybrid Mathematical Model for Data Classification and Prediction: Case study COVID-19

The increase of data availability has stimulated researchers to benefit from this data in predicting the hidden pattern for knowledge discovery. Data classification and machine learning algorithms are becoming important tools used in knowledge discovery. In this paper, we propose a hybrid classification model that combines some features and parameters from a probabilistic model and some other parameters from a divide and conquer model in a linear one. In our model, we generate a set of functions related to the number of attributes and the value of each attribute. Afterwards, these functions are reduced according to the number of classes needed. We test our model on collected data about symptoms in people infected with COVID-19 in England. Our simulation results show an accuracy rate in the range 50-80%. We expect to increase the accuracy rate if we increase the size of data used or we increase the number of attributes. © 2022. All Rights Reserved.

Supporting participatory innovation during the COVID-19 pandemic: a comparative study of enterprise social media use

Purpose The need for accelerating innovation is exacerbated as organizations struggle to either adapt or perish in this unforgiving condition due to the COVID-19 disruption. To address this issue, many organizations have embraced employee-driven participatory innovation to survive and thrive albeit the uncertainties. This study aims to investigate the role of enterprise social media (ESM) in supporting and facilitating these efforts. Design/methodology/approach This study first identified the underlying mechanisms that allow ESM use to foster and maintain participatory innovation and then reexamined how these mechanisms played out during the COVID-19 lockdown restrictions. The data was collected through a questionnaire in two phases, before and during work-from-home mandates, and the results were analyzed and compared to capture similarities and differences. Findings The results revealed that innovation culture and management support mediated the effects of ESM use on three measures of innovation productivity in both conditions. Interestingly, the effect of ESM use was more prominent in driving innovation in the work-from-home condition. This effect was not limited to the direct effect of ESM use on innovation productivity but on innovation culture and management support as well. Originality/value The results suggest that ESM offer a potentially useful path to support and enable employees to participate in the innovation processes, especially when they work remotely or in a distributed team. More generally, this paper should be of interest to researchers and practitioners interested in understanding, implementing and evaluating enterprise social software applications and encouraging employee-driven participatory innovation.

Praxis of knowledge-management and trust-based collaborative relationships in project delivery: mediating role of a project facilitator

Purpose>Critical knowledge and lessons learnt from the delivery of infrastructure projects have often remained untapped mainly due to the transient and fragmented nature of construction delivery. The main aim of this paper is to investigate the mediating role of a project facilitator in attenuating disruptions in knowledge flows during the delivery of an infrastructure project.Design/methodology/approach>An inductive case-study method is employed in examining the mediating role of the facilitator in an infrastructure project. Content analysis was undertaken by coding the data derived from eight focus group interactions, 23 semi-structured interviews and 24 documentary sources from workshops using NVivo 12 plus.Findings>(1) The project facilitator provided a coherent context to re-invent the narratives (i.e. behaviours and events) by creating a forum for understanding critical problems and stimulating constructive dialogue and intervention. (2) The project facilitator leveraged on both explicit and tacit knowledge within the team, leading to improvement in the proactive management of emergent technical, operational and behavioural challenges, and (3) The project facilitator sustained a valuable intervention in attenuating disruptions in knowledge flows for problem-solving, relationship-management, best-practice strategies, coaching and leadership, as well as reflexive practice.Originality/value>The novelty of this research is that a facilitator is used as the “knowledge-broker” in a multi-party infrastructure delivery team assembled using a traditional lump-sum contract framework. Facilitators have only previously been used in collaborative contract environments like alliancing and partnering.