Lowest theme vowels or highest roots? An ‘unaccusative’ theme-vowel class in Slovenian

This paper focuses on the e/i theme vowel class of verbs in Slovenian to bring together two seemingly unrelated debates; (i) the debate on the status of derivational affixes as roots within the framework of Distributed Morphology and (ii) the debate on the correlation between theme vowel classes with certain argument structures in Slavic. Focusing on Slovenian, our core data will come from active l-participles that are used adjectivally as an unaccusativity diagnostic. We take these l-participles to create a list of 109 unaccusative verbs. We show that (i) no unaccusative verbs belong to the two largest theme vowel classes in Slovenian (a/a and i/i), whereas (ii) the two big theme vowel classes tend to get accusative arguments quite frequently. Most importantly, (iii) the e/i-class stands out since more than one half of the unaccusative sample falls into. The e/i-class is furthermore the only theme vowel class whose theme vowel surfaces in adjectival l-participles, the theme vowel class to which inchoatives belong and behaves uniformly with respect to stress. Based on the uniform behavior of the e/i-class which sets it apart from other theme vowel-classes, we will argue that the vowel of this class is better analyzed as a root.

Gathering Global Perspectives to Establish the Research Priorities and Minimum Data Sets for Degenerative Cervical Myelopathy: Sampling Strategy of the First Round Consensus Surveys of AO Spine RECODE-DCM

Study Design Survey. Introduction AO Spine Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy (RECODE-DCM) is an international initiative that aims to accelerate knowledge discovery and improve outcomes by developing a consensus framework for research. This includes defining the top research priorities, an index term and a minimum data set (core outcome set and core data elements set – core outcome set (COS)/core data elements (CDE)). Objective To describe how perspectives were gathered and report the detailed sampling characteristics. Methods A two-stage, electronic survey was used to gather and seek initial consensus. Perspectives were sought from spinal surgeons, other healthcare professionals and people with degenerative cervical myelopathy (DCM). Participants were allocated to one of two parallel streams: (1) priority setting or (2) minimum dataset. An email campaign was developed to advertise the survey to relevant global stakeholder individuals and organisations. People with DCM were recruited using the international DCM charity Myelopathy.org and its social media channels. A network of global partners was recruited to act as project ambassadors. Data from Google Analytics, MailChimp and Calibrum helped optimise survey dissemination. Results Survey engagement was high amongst the three stakeholder groups: 208 people with DCM, 389 spinal surgeons and 157 other healthcare professionals. Individuals from 76 different countries participated; the United States, United Kingdom and Canada were the most common countries of participants. Conclusion AO Spine RECODE-DCM recruited a diverse and sufficient number of participants for an international PSP and COS/CDE process. Whilst PSP and COS/CDE have been undertaken in other fields, to our knowledge, this is the first time they have been combined in one process.

Reservoir Parameters Estimation with Some Log Curves of Qiongdongnan Basin Based on LS-SVM Method

Reservoir porosity and permeability are considered as very important parameters in characterizing oil and gas reservoirs. Traditional methods for porosity and permeability prediction are well log and core data analysis to get some regression empirical formulas. However, because of strong non-linear relationship between well log data and core data such as porosity and permeability, usual statistical regression methods are not completely able to provide meaningful estimate results. It is very difficult to measure fine scale porosity and permeability parameters of the reservoir. In this paper, the least square support vector machine (LS-SVM) method is applied to the parameters estimation with well log and core data of Qiongdongnan basin reservoirs. With the log and core exploration data of Qiongdongnan basin, the approach and prediction models of porosity and permeability are constructed and applied. There are several type of log data for the determination of porosity and permeability. These parameters are related with the selected log data. However, a precise analysis and determine of parameters require a combinatorial selection method for different type data. Some curves such as RHOB,CALI,POTA,THOR,GR are selected from all obtained logging curves of a Qiongdongnan basin well to predict porosity. At last we give some permeability prediction results based on LS-SVM method. High precision practice results illustrate the efficiency of LS-SVM method for practical reservoir parameter estimation problems.

Development of HFU-based permeability prediction models using core data for characterisation of a heterogeneous Oligocene sand in the Nam Con Son basin

Core data by both routine and special core analysis are required to understand and predict reservoir petrophysical characteristics. In this research, a total number of 50 core plugs taken from an Oligocene sand (T30) in the Nam Con Son basin, offshore southern Vietnam, were tested in the core laboratory of the Vietnam Petroleum Institute (VPI). The results of routine core analysis (RCA) including porosity and permeability measurements were employed to divide the study reservoir into hydraulic flow units (HFUs) using the global hydraulic elements (GHEs) approach. Based on five classified HFUs, 16 samples were selected for special core analysis, i.e., mercury injection capillary pressure (MICP) and grain size analyses for establishing non-linear porosity-permeability model of each HFU based on Kozeny-Carman equation, which provides an improved prediction of permeability (R2 = 0.846) comparing to that by the empirical poro-perm relationship (R2 = 0.633). In addition, another permeability model, namely the Winland R35 method, was applied and gave very satisfactory results (R2 = 0.919). Finally, by integrating the results from MICP and grain size analyses, a good trendline of pore size distribution index (λ) and grain sorting was successfully obtained to help characterise the study reservoir. High λ came with poor sorting, and vice versa, the low λ corresponded to good sorting of grain size.

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

Prerequisites of Personal Health Record for Chronic Kidney Disease: A Scoping Review and Evaluation of the Content Validity

Background: It is obvious that the Personal Health Record (PHR) is a major cornerstone for “improving the self-management of patient”. However, lack of an effective and comprehensive personal health record system prohibits the widespread use of PHRs. The aim of this study was to identify the core data sets and required functionalities for designing a PHRs for chronic kidney disease (CKD) management and assess their validity. Methods: It was a study including two phases. In the initial phase, a scoping review was conducted with the aim of determination the core data sets and required functionalities for designing PHRs. Then in the second phase, the validity of data items and functionalities was determined by 25 multidisciplinary experts. Results: 22 studies were eligible after screening 1335 titles and abstracts and reviewing 88 full texts. We determined 20 core data set and 8 required functionalities of PHRs. From the perspective of experts, ‘health maintenance’ and ‘advance directives’ were most often marked as useful but not essential, while ‘test and examination’, ‘medication list’ and ‘diagnosis and comorbid conditions” were predominantly considered as essential by all experts (n=25,100%). Conclusion: This research is a step that we have taken to identify prerequisites that could be used for the design, development, and implementation of an effective and comprehensive electronic personal health record.