Common Vocabulary Research Articles

Control Systems Cyber Security Reference Architecture (RA) for Critical Infrastructure: Healthcare and Hospital Vertical Example

A reference architecture (RA) provides a common frame of reference with a common vocabulary, reusable designs, and principles that may be applied to future architectures. It can promote re‐use of best practices, improve interoperability, and improve awareness of a system under development of the same mindset. The next version of the Department of Defense (DoD) Chief Information Officer (CIO) Cyber Security Reference Architecture (CSRA) will include an appendix for control systems. It provides a frame of reference for cybersecurity implementations based on generalizations of common principles that can provide a starting point for an organization's architecture effort, inform decision‐making, suggest governance, and help define future policy decisions for control systems. The appendix is based on the outcome of the MOSIACS Joint Capability Technology Demonstration (JCTD), which provides the initial cyber defensive capability framework for integrations of Commercial‐Off‐The‐Shelf (COTS) and Government‐Off‐The‐Shelf (GOTS) components to form a control system cyber defensive solution. The MOSAICS capability was successfully demonstrated in the energy sector critical infrastructure vertical on a power system in August 2021. Its applicability embodied delivery methods for technologies in other essential infrastructure sectors, repeatable playbooks for automated Courses of Action (COA), best practices, and templates for cyber security requirements for control systems. The advantages of the RA are to enable better decision‐making and policy‐making support about cybersecurity for control systems. This paper demonstrates the RA used in the Healthcare and Public Health sector's critical infrastructure vertical to evaluate concepts such as Zero Trust (ZT) and Defense‐in‐Depth (DiD) architecture principles related to policymaking.

Identifying competencies for future graphic design graduates in Malaysia: A Delphi study

Traditionally, graphic design (GD) education focuses on the training of technical production skills to prepare graduates for entry-level employment. However, due to the new challenges and the expanding opportunities of emerging practices, GD graduates are expected to master a wide range of additional competencies beyond traditional scope. The study aims to identify the competencies required by future GD graduates in the context of Malaysia. A two-round modified Delphi technique was used to gain consensus from a panel of experts consisted of design educators and industry practitioners regarding the competencies needed by the graduates for effective work performance. A total of 108 competencies were generated. All accepted competencies were ranked ‘very important’ and ‘extremely important’ by at least 75 percent of the respondents in Round Two. These competencies could be classified into 29 subdomains under five components, i.e., cognitive competence, functional competence, personal competence, ethical competence, and meta-competencies. The findings of this study are useful for enhancing the education and employability of graphic designers as they provide a common vocabulary to relevant stakeholders about the standards of performance for new entrants to the GD profession.

Where Is Ethology Heading? An Invitation for Collective Metadisciplinary Discussion.

Simple SummaryI analyzed the current state of Ethology (i.e., the study of animal behavior and cognition) from the researchers’ perspective through an online questionnaire that was responded to by almost a hundred participants. Despite that the number of the respondents was small, it is interesting to highlight some of the results, since they align with some published analyses. First, not many basic concepts of the discipline, nor its denomination, reached homogeneous consensus. This is alarming because researchers need common vocabulary to communicate effectively between them. Second, despite the enormous biodiversity existent, the researchers seem to be biased towards studying primates, our own family species. Also, the apparatuses employed in the studies are not always adapted to the species’ specific characteristics, so our conclusions about their behavior or cognition might be biased. Fortunately, the technology needed to conduct further studies already exists. However, there is not enough funding or collaboration with engineers to make it real. Establishing big scale networking, adopting some research principles such as transparency, and broadening gender and ethnic diversity in research teams may help in adopting new endeavors.Many factors can impact the advancement of scientific disciplines. In the study of animal behavior and cognition (i.e., Ethology), a lack of consensus about definitions or the emergence of some current events and inventions, among other aspects, may challenge the discipline’s grounds within the next decades. A collective metadisciplinary discussion may help in envisioning the future to come. For that purpose, I elaborated an online questionnaire about the level of consensus and the researchers’ ways of doing in seven areas: Discipline name and concepts, species, Umwelt, technology, data, networking, and the impact of sociocultural and ecological factors. I recruited the opinion of almost a hundred of colleagues worldwide (N = 98), both junior and seniors, working both in the wild and in the lab. While the results were pitted against the literature, general conclusions should be taken with caution and considered as a first attempt in exploring the state of the discipline from the researchers’ perspective: There is no unanimity for the discipline’s name; 71.4% of the researchers reported there is limited consensus in the definition of relevant concepts (i.e., culture, cognition); primate species still predominate in publications whereas the species selection criteria is sometimes based on fascination, chance, or funding opportunities rather than on biocentric questions; 56.1% of the apparatuses employed do not resemble species’ ecological problems, and current tech needs would be solved by fostering collaboration with engineers. Finally, embracing the Open Science paradigm, supporting networking efforts, and promoting diversity in research teams may help in gathering further knowledge in the area. Some suggestions are proposed to overcome the aforementioned problems in this contemporary analysis of our discipline.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

BackgroundThe Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients.MethodsA nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract–transform–load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers.ResultsOut of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive.ConclusionsIn this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.

Harmonizing Race and Ethnicity Data to Facilitate Data Analysis of Pooled Environmental Health Studies

BACKGROUND AND AIM: Race and ethnicity are social constructs that function as necessary tools in environmental health research, helping to identify underserved populations. Despite much discussion of more inclusive ways for individuals to self-identify, there is insufficient consideration for organizing these diverse subgroups for use as data across multiple studies, facilitating comparisons. The HHEAR (Human Health Exposure Analysis Resource) Data Repository houses deidentified data of environmental and exposome studies. Data are harmonized to a common vocabulary to facilitate data pooling. Most HHEAR studies use variations of the current Office of Management and Budget (OMB) standards to place groups of people into categories. These different categorizations can hinder harmonization, as some studies separate race and ethnicity using a two-question format while others construct a combined format. Few incorporate country or region of origin. Additionally, OMB standards are extended to studies that take place outside the United States, which may not have originally used these categorizations. METHODS: We conducted a review of published literature, standard ontologies used by research collaborations, and national reviews from countries outside the United States to review the harmonization of race and ethnicity and identify methods to facilitate data pooling in the HHEAR repository. RESULTS:As there was no clear consistency in mappings across literature, we created our own broad data standards to accommodate the diverse definitions incorporating race and ethnicity on both national and international levels. We encourage more standardized reporting in future studies in order to facilitate harmonization. CONCLUSIONS:Large, diverse datasets allow for investigations with larger sample sizes and greater exposure variability. By improving and standardizing methodology for data harmonization, environmental health studies can promote social justice by informing policy to allocate time and resources to at-risk communities and, in turn, reduce health disparities. KEYWORDS: Dictionary Mapping, Knowledge Modeling, Data Standards, Harmonization, Race, Ethnicity

Assessing the Role of Cyberbiosecurity in Agriculture: A Case Study.

Agriculture has adopted the use of smart technology to help meet growing food demands. This increased automation and associated connectivity increases the risk of farms being targeted by cyber-attacks. Increasing frequency of cybersecurity breaches in many industries illustrates the need for securing our food supply chain. The uniqueness of biological data, the complexity of integration across the food and agricultural system, and the importance of this system to the U.S. bioeconomy and public welfare suggests an urgency as well as unique challenges that are not common across all industries. To identify and address the gaps in awareness and knowledge as well as encourage collaborations, Virginia Tech hosted a virtual workshop consisting of professionals from agriculture, cybersecurity, government, and academia. During the workshop, thought leaders and influencers discussed 1) common food and agricultural system challenges, scenarios, outcomes and risks to various sectors of the system; 2) cyberbiosecurity strategies for the system, gaps in workforce and training, and research and policy needs. The meeting sessions were transcribed and analyzed using qualitative methodology. The most common themes that emerged were challenges, solutions, viewpoints, common vocabulary. From the results of the analysis, it is evident that none of the participating groups had available cybersecurity training and resources. Participants were uncertain about future pathways for training, implementation, and outreach related to cyberbiosecurity. Recommendations include creating training and education, continued interdisciplinary collaboration, and recruiting government involvement to speed up better security practices related to cyberbiosecurity.

Design of Generalized Search Interfaces for Health Informatics

In this paper, we investigate ontology-supported interfaces for health informatics search tasks involving large document sets. We begin by providing background on health informatics, machine learning, and ontologies. We review leading research on health informatics search tasks to help formulate high-level design criteria. We use these criteria to examine traditional design strategies for search interfaces. To demonstrate the utility of the criteria, we apply them to the design of ONTology-supported Search Interface (ONTSI), a demonstrative, prototype system. ONTSI allows users to plug-and-play document sets and expert-defined domain ontologies through a generalized search interface. ONTSI’s goal is to help align users’ common vocabulary with the domain-specific vocabulary of the plug-and-play document set. We describe the functioning and utility of ONTSI in health informatics search tasks through a workflow and a scenario. We conclude with a summary of ongoing evaluations, limitations, and future research.

Environmental Impact Bonds: a common framework and looking ahead

Abstract A frequent barrier to addressing some of our world’s most pressing environmental challenges is a lack of funding. Currently, environmental project funding largely comes from philanthropic and public sources, but this does not meet current needs. Increased coordination and collaboration between multiple levels and sectors of government, in addition to private sector funding, can help address the environmental funding challenge. New financial tools and strategies can enable this transition and facilitate uptake of innovative solutions. One such mechanism, the Environmental Impact Bond (EIB), is an emerging financial tool with the potential to transform the environmental funding landscape. However, these financial instruments are not well understood or recognized beyond those actively involved in EIB projects or in the field of conservation finance. As EIBs gain momentum, there is a clear need for a common framework, including definitions and nomenclature, research needs, and outlook for the future. In this paper, we define EIB mechanics, elucidate the difference between EIBs and Green Bonds, and propose a common vocabulary for the field. Drawing on first-hand experience with the few EIBs which have been deployed, we review and assess lessons learned, trends, and paths for the future. Finally, we propose a set of future targets and discuss research goals for the field to unify around. Through this work, we identify a concrete set of research gaps and objectives, providing evidence for EIBs as one important tool in the environmental finance toolbox.

A TOPOLOGICAL FORMALISM FOR QUANTITATIVE ANALYSIS OF DESIGN SPACES

AbstractThe objective of this paper is to present a mathematically grounded description of the two topological spaces for the design problem and the design solution. These spaces are derived in a generalized form such that they can be applied by researchers studying engineering design and developing new methods or engineers seeking to understand the influence that changes in the problem space have on the solution space. In addition to the formal definitions of the spaces, including assumptions and limitations, three types of supported reasoning are presented to demonstrate the potential uses. These include similarity analysis to compare spaces, an approach to sensitivity analysis of the solution space to changes in the problem space, and finally a distance measure to determine how far a current proposal is to the feasible solution space. This paper is presented to establish a common vocabulary for researchers when discussing, studying, and supporting the dyadic nature of engineering design (problem-solution co-evolution).

Glossary of terms and concepts relating to diversity, equity, and inclusion to reflect on, debate, and raise awareness of: A report from the Society for Vascular Surgery Diversity, Equity, and Inclusion Committee

The presented glossary highlights the concepts and terminology related to diversity, equity, and inclusion. Although this list should not be considered all-inclusive, it will provide our vascular surgical community with a common vocabulary to help propel change toward improved diversity, equity, and inclusion policies, practices, and culture. As these concepts and terminology find their place in our everyday lives, the Society for Vascular Surgery and its leadership believe that expanding the diversity, equity, and inclusion of vascular surgical professionals is a mission-critical step. This mission will help us improve patient experience through successfully delivering culturally sensitive care for our diverse community. Now is the time.

Über ból und symptomy… Wortschatz im Polnischunterricht für deutschsprachige Rettungskräfte

This paper focuses on the lexical side of the emergency communication of German paramedics who are learning Polish as a foreign language for professional purposes. The aim of the study is to find out which lexical units arise during language courses and how often. To what extent can the acquired vocabulary be used to develop dedicated programmes for foreign language acquisition in medical emergency communication? The analysis shows that the vocabulary collected here depends on the subject, such as ból (pain) or leki (medicines). According to the participants, symptomy (symptoms) and duszności (shortness of breath) are also part of the common vocabulary. Emergency medical communication can be carried out with the question pronouns kiedy (when) and gdzie (where). Among the most frequently repeated lexical units of vital parameters during patient transfer are saturacja (oxygen saturation) and puls (pulse). The vocabulary collected here should help in preparing a description of the competences which should form part of a language course for professional purposes.

Analysis of the Level of Interest of Junior High School Students in Learning Basic Japanese Language

Japanese is one of the most popular languages after English. Its culture that captivates people's hearts, makes interest in the language increase, both from children and adults. Until now, learning Japanese has become the program of choice at the high school level to universities. This study aims to analyze the interest of junior high school students in Japanese. Descriptive qualitative research method. The data collection technique used qualitative descriptive and provided some introductory vocabulary, numbers, and some common Japanese vocabulary. The results showed that students' knowledge of basic Japanese when given the pretest was 66.67%. After students were given material in the form of power points about basic Japanese, students' knowledge increased by 70.37%. The conclusion obtained is that students already have sufficient knowledge of basic Japanese such as introductions, numbers, and basic vocabulary. It can be seen from the increase in the pre-test and post-test that has been given only an increase of 11%. In addition, it can be seen from the enthusiasm of students in interactive question and answer sessions during the learning process. It is hoped that this research can become a consideration regarding the choice of Japanese language programs up to junior high school level

The Strengths of Reading Materials in Students’ Worksheet Used at Second Grade of SMA N 9 Kota Jambi

The aims of the study were to find out the strengths of reading materials in students’ worksheet: Talk Active 2 used at Second Grade of SMA N 9 Kota Jambi meet the criteria of good learning materials. Those criteria are: (1) The materials make clear the link between the classroom and the wider world (2) The materials foster independent learning (3) The materials focus the learner on the the opening process (4) The materials are readily available. The student worksheet was written by Fx. Sukristiono, Nursahid, Sudarso, and published by Yudhistira. The research is expected to give insights of understanding the quality of reading materials. This study was designed as a documentary analysis in the form of descriptive qualitative research in which the result would be described in the form of words. The data were collected from the reading section in the students’ worksheet. As supplementary tools of the instrument, forms of checklist were used during the research analysis. The researcher concluded that most of students easy to understand about content of the text but, they still have difficulty in understand the reading materials about definitions, generic structures and functions of each text. Because, most of the texts only present the example of each text. The students need the worksheet should be clearer in reading materials. Not only provide a lot of exercise but also the clear explanations about the definitions, generic structure, purpose, language features and usage of tenses.The result of identified reading materials in students’ worksheet shows some strengths, they are; 1) Common vocabulary for second grade of senior high school, 2) Each text types in students’ worksheet has more than one text, 3) There are learning materials in students’ worksheet.Key Words: Strengths, Reading Materials, Students’ Worksheet.

A Proposed Ontology-Based Generic Context Model for Ubiquitous Learning

Context modeling is the keystone to enable the intelligent system to adapt its functionalities properly to different situations. As such, a representation mechanism that allows an adequate manipulation of this kind of information is required, and diverse approaches have been introduced; however, what takes more value and is being positioned as a standard is the ontology-based context modeling because it presents a common understanding vocabulary for a specific domain. Hence, it might be beneficial to have a generic ontology to model context in this area. However, according to diverse works, there is no proposal of a generic context model for context-aware learning. For addressing this problem, several existing context models are studied to identify the essentials of context modeling, whereby an ontology-based generic context model is presented. The proposed ontology is evaluated in two ways. Firstly, scenarios are used to justify the feasibility of the model; then a comparative study and evaluation metrics are applied to assess the proposal.

Неологизмы в английской биржевой терминологии

The study aims to identify features of terms-neologisms belonging to the field of trading. Scientific novelty of the study lies in the fact that it is the first attempt at carrying out a comprehensive analysis of the English neologisms of the trading sublanguage. As a result, it is proved that the main source for the English trading neologisms is common literary vocabulary at its current development stage; there is a tendency to use abbreviations for nomination of new words and notions; compound terminological combinations dwindle and lose their relevance; computer technologies have a growing influence on formation of the considered sublanguage nomenclature.

COVID-19 vaccine delivery: an opportunity to set up systems for the future

The race to develop safe and effective SARS-COV-2 vaccines has moved with unprecedented speed. There are now multiple vaccines that have received emergency use authorization from the United States Food and Drug Administration and a host of candidates positioned for approval worldwide. Attention has now turned to allocation, distribution and verification of these vaccines, yet this focus exposes that the underlying infrastructure for global delivery and monitoring is threadbare and unevenly distributed. This presents both a barrier and an opportunity to deploy sustainable infrastructure. Major global stakeholders must convene quickly, collaborate, and collectively invest in global standards, legal models, common vocabularies and interoperable biometric-supported digital health technologies. As the COVID-19 vaccine effort scales, governments, private sector, and NGOs have the chance to place lasting resources needed for equitable and effective delivery that can pay dividends into the future.

Raising Students’ Vocabulary Mastery Using Selected Game: Using Chalk Down, Turn-around for EYL Students

Based on the 2013 curriculum, students are expected to master common vocabulary in academic text. However, in reality, the above goal has not been achieved. It can be proven by the fact that students still have a common vocabulary and using incorrect words. During the pre-observation, students often experience learning obstacles while learning English. The purpose of this study was to determine the use of Chalk Down, Turn Around Game for improving students’ vocabulary mastery at grade VIII SMP Negeri 1 Bambang. This study used a pre-experimental method in the form of one class given a pre-test and post-test. The number of students involved 20 students and selected using the total sampling technique. The results of the data showed that the mean score on the post-test is higher than the pre-test (85> 60.25), and the t-test value is higher than the t-table score (11,049> 2,092) at the level of increase of 0.05 with degree freedom (df) 19. It means that the use of the Chalk Down, Turn Around Game can improve students' vocabulary.

ДО ПИТАННЯ ПРО ЛЕКСИЧНІ ПРОБЛЕМИ ПЕРЕКЛАДУ ЗАГОЛОВКІВ НАУКОВИХ ПРАЦЬ

The article deals with the problem of researching translation of scientific paper titles, which includes consideration of the title as a specific type of concise text and translation of scientific and technical terminology. Both aspects are relevant at the time of rapidly increasing information flows and the development of various technologies and scientific challenges. The main sources of typical translation problems are incorrect reproduction of ambiguous common vocabulary, abbreviations, "false friends of the translator", paronyms, pseudo-internationalisms, foreign words and terms, various proper names and titles. Problems of translating titles are studied in a number of linguistic works. S. Barkhudarov, G. A Weikhman, O. M. Kovalenko, D. Bieber, and others made a significant contribution to the development of the problem. But, in our opinion, there are still unresolved issues, in particular, one of them - lexical problems of title translation and means of overcoming them. Therefore, the purpose of the article is to analyze the means of translation of scientific vocabulary in the titles in order to establish their expediency or inexpediency. The main translation techniques for lexical items are calquing, addition, omission, concretization, generalization and compensation. It can be noted that this type of translation of names requires not only linguistic, but also scientific and technical, and journalistic knowledge. Knowledge and correct use of the above translation methods will help to avoid incorrect transmission of English vocabulary in the Ukrainian language, as well as distortion of the logical content. The translation of the titles of English popular science articles is a complex but interesting process, which, in principle, is a careful work on many lexical and grammatical difficulties. The title, like a mirror, reflects all the difficulties of translating English text.

2021 ACC/AHA Key Data Elements and Definitions for Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Heart Failure).

2021 ACC/AHA Key Data Elements and Definitions for Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Heart Failure).

Robust Data-Driven Machine-Learning Models for Subsurface Applications: Are We There Yet?

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Data analytics (DA)—Sophisticated data collection and analysis to understand and model hidden patterns and relationships in complex, multivariate data sets Machine learning (ML)—Building a model between predictors and response, where an algorithm (often a black box) is used to infer the underlying input/output relationship from the data Artificial intelligence (AI)—Applying a predictive model with new data to make decisions without human intervention (and with the possibility of feedback for model updating) Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017). While the terms DA, ML, and AI are often used interchangeably, it is important to recognize that ML is basically a subset of DA and a core enabling element of the broader application for the decision-making construct that is AI. In recent years, there has been a proliferation in studies using ML for predictive analytics in the context of subsurface energy resources. Consider how the number of papers on ML in the OnePetro database has been increasing exponentially since 1990 (Fig. 1). These trends are also reflected in the number of technical sessions devoted to ML/AI topics in conferences organized by SPE, AAPG, and SEG among others; as wells as books targeted to practitioners in these professions (Holdaway 2014; Mishra and Datta-Gupta 2017; Mohaghegh 2017; Misra et al. 2019). Given these high levels of activity, our goal is to provide some observations and recommendations on the practice of data-driven model building using ML techniques. The observations are motivated by our belief that some geoscientists and petroleum engineers may be jumping the gun by applying these techniques in an ad hoc manner without any foundational understanding, whereas others may be holding off on using these methods because they do not have any formal ML training and could benefit from some concrete advice on the subject. The recommendations are conditioned by our experience in applying both conventional statistical modeling and data analytics approaches to practical problems.