Domains Of Use Research Articles

DQM: Data Quality Metrics for AI Components in the Industry

In industrial settings, measuring the quality of data used to represent an intended domain of use and its operating conditions is crucial and challenging. Thus, this paper aims to present a set of metrics addressing this data quality issue in the form of a library, named DQM (Data Quality Metrics), for Machine Learning (ML) use. Additional metrics specific to industrial application are developed in the proposed library. This work aims also to assess various data and datasets types. Those metrics are used to characterize the training and evaluating datasets involved in the process of building ML models for industrial use cases. Two categories of metrics are implemented in DQM: inherent data metrics, are the ones evaluating the quality of a given dataset independently from the ML model such as statistical proprieties and attributes, and model dependent metrics which are those implemented to measure the quality of the dataset by considering the ML model outputs such the gap between two datasets in regards to a given ML model. DQM is used in the scope of the Confiance.ai program to evaluate datasets used for industrial purposes such as autonomous driving.

Semantic Components of English Directive Speech Act Verbs (Order, Command, Instruct)

This research focuses on synonymous English speech act verbs that mean “giving order” (directive). The verbs analyzed in this study are those with very close synonymy, namely order, command, and instruct. The aim of this research is to describe in more detail the semantic behavior of the directive English speech act verbs through their semantic component. This semantics study is qualitative descriptive research. The data in this study were collected by using corpus linguistics and observation methods with note-taking techniques. The data source is the Corpus of Contemporary American English (COCA). There are 300 citations for each verb analyzed, which making in total 900 citations analyzed in the study. The study found that the verbs order, command, and instruct has eight semantic features/components that differentiate those three verbs, namely the components of authority, the way of saying, form of order, the duration of orders, level of urgency, actor and undergoer positions, domain of use, compliance level.

A Standard Measure of Structural Racism, Do We Have One? Can We Have One? A Narrative Review of Commonly Used Measures and Domains of Use.

A lack of a "gold standard" operationalized index to measure structural racism (SR) in the current literature limits the comparison of the evidence available. This study aims to synthesize the measures of SR from the current literature to identify the measures used to date, study the indicators included, and investigate its expanding domain. A literature search of original quantitative studies in the Google Scholar and PubMed databases for articles dated January 1, 2000-July 31, 2023, was conducted with search terms: ["Institutionalized Racism" OR "Systemic Racism", OR "Structural Racism"] AND "Health" AND "United States." The studies were summarized and extracted based on the type of SR index used, the domains of SR incorporated, and the health outcomes studied. A total of 74 articles were included in the final review. The historical redlining score, G-statistics, index of concentration, and structural racism index were common quantifiable measures of SR. These indices capture 56 indicators across 11 significant domains to measure SR. Similarly, SR indices are being used mostly to study the impact of SR on cardiovascular diseases and other chronic health conditions, women's and maternal health-related issues, and COVID-19 outcomes. Multiple indices have been developed to capture SR, and since the COVID-19 pandemic, we have observed an increased interest in understanding health disparities through the lens of SR. With the rise in evidence on experiences of minority races related to racism, there is a high need for a standard approach to measuring SR.

Multilingualism in Burundi: Languages and their Domains of Use

This study describes multilingualism in Burundi, focusing on the social contexts in which each language is used. The main instrument used was a 16-item questionnaire coupled with informal discussions, and the informants were 20 volunteer students admitted to the National School of Administration – the ENA school for short, a government institution assigned to set up and implement an English Language training programme that teaches English to all civil servants from existing ministries. The questionnaire derives from Fishman’s domains of language use, which is the frame adopted for the analysis. The findings reveal that four languages co-exist in Burundi, i.e., Kirundi, the ancestral language; Belgian French, the coloniser’s language; Kiswahili, a trade language dominant in neighbouring countries; English, the working language for various East African regional institutions, including the East African Community, and the Economic Community of the Great Lake Countries. While Kirundi is dominant in the family, religion, and friendship domains, French, a colonial language, reigns in various schools in the country, and English, dominant regionally, reigns supreme in the ENA school.

Philippine Englishes in the Sino‐Philippine Lannang context: Towards a concentric‐pluricentric interactional‐interplanar model of English

AbstractThis article explores the relationship between Philippine English and the Lannangs, individuals with Filipino and Southern Chinese cultural heritage. It highlights the multifaceted nature of this English variety by discussing how it interacts with non‐English languages in contemporary Lannang communities located in Manila, Iloilo and Cebu. Using corpus data from 10 Lannang linguistic varieties used in these areas, I found that Philippine English has assumed three primary linguistic roles (that of a lexifier, a substrate and a lexical contributor)—dynamic roles that are conditioned by the (sociohistorical) context, domain of use and conventions of the specific community. In conjunction with existent accounts of non‐Lannang Philippine English(es), my findings justify complexity‐based representations or models of Philippine English that has multiple levels (degrees of English influence) and centres (ethno‐regional context) that interact with each other across different planes (e.g. social), such as the concentric‐pluricentric interactional‐interplanar (CPII) model proposed in this paper. They problematize and challenge the notion of a monolithic Manila‐based ‘Philippine English.’ The proposed model presents a framework/blueprint for analyzing English in multilingual settings.

Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts

This research investigates the perspectives of using artificial intelligence writing software (AIWS) in professional contexts, focusing on academic and non-academic writers. These two groups, while standing to gain increased productivity through the adoption of AIWS, also express concerns regarding the widespread implementation of this technology. Notably, artificial intelligence (AI) writing tech’s impact on content creation has been profound, with its swift grammatically accurate content generation. This adoption, however, remains controversial. The study employs a quantitative approach, combining technology acceptance model and new computer game attitude scale. This approach allows us to discern implications of using AI-powered writing tools while accounting for possible differences in different domains of use. Through a survey of 219 participants, spanning academia and business, the study explores attitudes and willingness to use AIWS. Findings yield insights into non-academic writers’ readiness and implications of AIWS adoption. Business, non-academic professionals view AIWS as a tool for efficiency and content quality, while writers in academic contexts express concerns about biases, manipulation, and job displacement. The study contributes to AIWS understanding, benefiting developers, educational institutions, and content creators, and elucidates differing attitudes and age dynamics between academics and professionals. The research underscores the multifaceted influence of AIWS, providing a foundation for future exploration in this emerging domain, as well as practical applications for industries and educational institutions.

Resilience through language? A case study of three minority communities in Georgia

ABSTRACT The concept of resilience has been employed as an analytical tool in a wide range of scientific disciplines and fields from the natural sciences to social sciences and humanities, but within linguistics it is a relatively new and so far, rarely applied concept. This paper is intended as a contribution to fill this gap through a sociolinguistic study of three minority language communities in Georgia - Armenians, Azerbaijanis and Chechens. The sociolinguistic conditions of the three communities in Georgia partially overlap, but also show interesting differences. In order to observe the impact that minority languages possibly have on forming resilient communities, we have interviewed members of these three minority groups using semi-structured questionnaires. We concentrate on two important domains of use, namely education and work, and also explore language attitudes and ideologies. The results of our study are mixed: minority language speakers occasionally benefit from their language knowledge, and their positive attitudes towards their own languages strengthen their linguistic and sociocultural identity. Nevertheless, linguistic discrimination is also part of their every-day experience in a majority society that highly values the national language Georgian.

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.

The Role of Computer Teachers in the Kasbah of Amman Schools in Developing Digital Intelligence in Distance Learning among their Students from the Point of View of their Principals

The present study aimed to investigate the role of computer teachers in Kasbah Amman schools in developing digital intelligence in distance learning among their students from the point of view of their principals. The descriptive survey methodology was adopted. The validity and study sample consisted of (120) principals, and the questionnaire that was developed reliability of the tool were assured. The results of the study showed that role of the computer teachers in developing digital intelligence in distance learning among their students from the point of view of their principals came to a large degree, and that the highest domain were the domain of digital security and safety development, followed by the domain of use and digital literacy, and there were no statistically significant differences attributed to the two variables (gender, and experience).

Fast and Fourier: extreme mass ratio inspiral waveforms in the frequency domain

Extreme Mass Ratio Inspirals (EMRIs) are one of the key sources for future space-based gravitational wave interferometers. Measurements of EMRI gravitational waves are expected to determine the characteristics of their sources with sub-percent precision. However, their waveform generation is challenging due to the long duration of the signal and the high harmonic content. Here, we present the first ready-to-use Schwarzschild eccentric EMRI waveform implementation in the frequency domain for use with either graphics processing units (GPUs) or central processing units (CPUs). We present the overall waveform implementation and test the accuracy and performance of the frequency domain waveforms against the time domain implementation. On GPUs, the frequency domain waveform takes in median 0.044 s to generate and is twice as fast to compute as its time domain counterpart when considering massive black hole masses ≥2×106M⊙ and initial eccentricities e0 > 0.2. On CPUs, the median waveform evaluation time is 5 s, and it is five times faster in the frequency domain than in the time domain. Using a sparser frequency array can further speed up the waveform generation, reaching up to 0.3 s. This enables us to perform, for the first time, EMRI parameter inference with fully relativistic waveforms on CPUs. Future EMRI models, which encompass wider source characteristics (particularly black hole spin and generic orbit geometries), will require significantly more harmonics. Frequency domain models will be essential analysis tools for these astrophysically realistic and important signals.

Multi-Source Data Integration for Segmentation of Unannotated MRI Images.

Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various clinical applications. However, training these models is conditioned on the availability of abundant annotated data. Even if we annotate enough data, MRI images display considerable variability due to factors such as differences among patients, MRI scanners, and imaging protocols. This variability necessitates retraining neural networks for each specific application domain, which, in turn requires manual annotation by expert radiologists for all new domains. To relax the need for persistent data annotation, we develop a method for unsupervised federated domain adaptation using multiple annotated source domains. Our approach enables the transfer of knowledge from several annotated source domains for use in an unannotated target domain. Initially, we ensure that the target domain data shares similar representations with each source domain in a latent embedding space by minimizing the pair-wise distances between the distributions for the target and the source domains. We then employ an ensemble approach to leverage the knowledge obtained from all domains to build an integrated outcome. We perform experiments on two datasets to demonstrate our method is effective. Our implementation code is publicly available: https://github.com/navapatn/Unsupervised -Federated-Domain-Adaptation-for-Image-Segmentation new.

Applications of Hyaluronic Acid (HA) in Dental Implant Treatment: A Systematic Review

Introduction: Hyaluronic acid (HA) has a long history and is widely used in cosmetics, medicine, and dermatology. This molecule is still considered relatively new in the field of dentistry. This study aimed to assess the application of HA in dental implant treatment. Method: Search in the multiple indexed databases such as Pubmed, COCHRANE, and Scopus was conducted up until August 2022 using the keywords “hyaluronic acid”, “hyaluronan,” and “dental implant.” Results: The literature search identified 816 articles, and 17 were selected in this study. Three domains of use of HA in dental implant treatment were identified: surface modification of implant surface, treatment after insertion of a dental implant, and bone graft/membrane material. There are eight randomized control trials and nine non-randomized control trials included in this study. Only six studies showed statistically significant results with HA groups. Conclusion: Overall, there are positive findings on the application of HA in dental implant treatment, showing it can be used in dental implantology, with multiple categories of uses.

Designing an acceptable and fair carbon tax: The role of mental accounting

Despite its potential for curbing greenhouse gas emissions, carbon taxation encounters strong public resistance. However, acceptability depends on how tax revenues are used. We test the hypothesis that mental accounting theory can both explain systematic patterns in citizens’ preferences, such as the support for environmental earmarking, and help design a carbon tax scheme that is both acceptable and fair. Across six experiments conducted in the United Kingdom and in France (Ntotal = 7100), we show that: (a) There is an acceptability boost when the use of tax revenues matches the tax domain thematically (e.g., allocating carbon tax revenues to green projects), as demonstrated by an interaction effect between the tax domain and the expenditure domain on the level of tax support. This result is consistent with the use of a mental accounting heuristic, by which people create mental budgets where the origin of revenues is matched thematically with their domain of use. (b) Carbon tax acceptability varies with the proportion of tax revenues earmarked for green projects. (c) A mixed carbon tax scheme, in which most revenues are earmarked for green projects and the rest is redistributed to low-income households to be spent on sustainable expenses, receives most support among the tested options. We also demonstrate the robustness of the mental accounting heuristic in two ways: by showing that the preference for environmental earmarking of carbon taxes is observed across all relevant subsections of the population, and that mental accounting also appears to shape preferences for health-related earmarking of tobacco taxes, and social-related earmarking of inheritance taxes.

Taxonomy of market-level space organizations

Academic and industry experts build their careers on accurate and precise definitions and meanings of domain-specific terminology, whereas novice practitioners and lay-persons often conflate similar or similar-sounding terms to describe concepts in the same domain of interest. This is common in the space sector when individuals of different academic or practitioner backgrounds discuss “commercial” topics. In English, as in other languages, the word “space” is often followed by words such as “industry,” “industry segment,” “industrial base,” “market,” “ecosystem” and others. The practice of casually using words with ambiguous meanings may often be appropriate for informal discussion if it facilitates the communication and exchange of ideas and all parties generally understand the implications of the casual usage. This practice, however, is problematic for detailed and analytical applications requiring precision and order of thought. Grounded in contemporary organization theory and economic practice, this paper provides background discussions of different levels of analysis that characterize embedded hierarchical social systems of organizations as they pertain to space. This paper then recommends a hierarchical taxonomy for defining specific terms that describe organizations and activities within the space domain for use by practitioners, analysts and academics requiring more precise language to communicate their market analysis ideas. This taxonomy places specific terms within each level of analysis discussed and provides definitions of each, with the intention of stimulating critical discussion and potential modifications or improvements.

Towards Understanding Tajikistan’s Sociolinguistically Complex Language Ecology: Historical Development, Current Status, Issues, Research, Policy and Practice

Tajikistan, at the heart of Central Eurasia, had a population of 7,563,687 in 2010, estimated recently to be almost 10,084,935. Named for its majority nationality, Tajikistan has many other nationalities, most with their own language. This article explores what is known about the historical, development and current status of multiple languages in Tajikistan’s linguistic tapestry. We provide a tentative overview of Tajikistan’s evolving language ecology from earliest times when a range of Eastern Iranian languages were dominant, to the reduced use of Eastern Iranian languages following the entry of Arabic and New Persian (a western Iranian language) into the ecology with the Arab conquest, and the subsequent entry of Turkic languages, and more recently the entry of Russian under the late Russian empire and its spread under the Soviet Union. Following independence in 1991, a shift in balance among domains of use of Tajik and Russian has been ongoing at the same time as international languages, especially English, have entered Tajikistan’s language ecology. We review the current state of knowledge about contemporary sociolinguistic dynamics, monolingualism and plurilingualism in society, where the titular language, Tajik/Persian, is in interaction with local, regional and global languages. Against the background of changing post-independence language and language-in-education policies, we discuss the prospects for monolingual, multilingual and plurilingual education in Tajikistan among efforts to promote the official language, Tajik, and to provide minority language education, while also developing proficiency in foreign languages in Tajikistan, through initiatives such as English-medium instruction.

From Linguicism to Language Attrition: The Changing Language Ecology of Gilgit-Baltistan

The study examines potential threats and prospects for the linguistic ecosystem of Balti and other local languages spoken in Gilgit-Baltistan, Pakistan. The researchers employ a mixed-methods approach in the study and the data is collected from 305 respondents through a survey questionnaire developed on Google forms. The respondents are either native Balti speakers or use Balti as their second or link language. The questionnaire used in the study contains both close-ended and open-ended questions. The findings of the study show that the linguistic ecosystem of Balti and other local languages is highly endangered and has been severely contaminated. These languages are losing their domains of use at an increased pace mainly due to the arrival of digital media in the region. Language attrition, mediated by relexification, is at its peak, and the future prospects for Balti and other minority languages of Gilgit-Baltistan seem quite bleak due to the growing influence of linguistic imperialism in the region which is being facilitated mainly through the system of education and the digital media.

Evaluation of Semecarpus kurzii Engler from Bay Islands for oxidative DNA damage protective activity and in vitro antioxidant potential

Introduction and Aim: Harnessing traditional knowledge of medicinal plants is important for the betterment of mankind. We must safeguard traditional knowledge from misuse by miscreants living in modern societies and knowledge must be available in the public domain for use in drug designing in the healthcare system. Since the beginning of recorded history, Indians have used plants as a source of medicine, and their cultural past is incredibly rich. This study was conducted to evaluate the oxidative stress-induced DNA damage prevention and antioxidant potential of Semecarpus kurzii found in the Bay Islands. Materials and Methods: The scavenging of superoxide, hydroxyl radicals, nitric oxide (NO), 2,2-diphenyl-2-picrylhydrazyl hydrate (DPPH) radicals, ferric reducing power (FRAP), and lipid peroxidation inhibition activity were tested to determine antioxidant activity. DNA damage inhibition test was used to evaluate the protection against oxidative DNA damage. Results: The S. kurzii extract showed dose-dependent scavenging of DPPH, superoxide anion, nitric oxide, hydroxyl radical and reducing power evaluated by comparing with standard antioxidants (ascorbic acid, alpha-tocopherol and BHT). However, the nitric oxide scavenging (IC50 =186.47) and superoxide scavenging (IC50 = 678.32) was more than that of DPPH (IC50 =28.03) and Hydroxyl radical (IC50 =89.10) scavenging capacity. In addition, S. kurzii extract and ascorbic acid showed lipid peroxidation inhibition activity. The extract also exhibited DNA protection which was confirmed by the DNA damage inhibition assay. Conclusion: Our results corroborated that, the methanolic extract of S. kurzii bark has substantial antioxidant activity and DNA damage inhibition. The potential antioxidant and oxidative DNA damage preventive activity could be due to the existence of polyphenolic compounds in the S. kurzii bark.

Using computer techniques for vibration damage estimation under stochastic loading using the Monte Carlo Method for aerospace applications

The main focus of this article is a review of legacy methods for vibration damage estimation under stochastic loading and extending research made by Dirlik and Bendat using two combined methods: FEM and Monte Carlo simulation, for which we used Python programming for aerospace applications. For some aircraft, regulated by the RTCA international aviation standard DO-160G (Environmental Conditions and Test Procedures for Airborne Equipment), stochastic loading is defined as one of the requirements. This article will focus on the stochastic loading impact on the fatigue life assessment made on a dummy sample, and frequency and time domain damage estimation shall be considered in parallel to compare both results. Additionally, dummy PSD responses shall be defined in the frequency domain for signal statistical parameters research. The article introduces Rainflow Cycle Counting methods in the frequency domain for procedures used commercially in aerospace applications. The first method introduced and developed further is the Dirlik method of Rainflow Cycle Counting in the frequency domain, which is the most popular method in commercial use. The second technique introduced and developed further was established by Bendat — the Narrow Band Method. The new empirical equation presented in this paper is the modification of the Narrow Band Method fitted for general use (narrow band, wide band, and white noise signals). A new approach for the integration of spectral moments is introduced in this paper, allowing for an accurate evaluation of the signal statistic parameters in the frequency domain for use in the modified Dirlik and Narrow Band methods. Research results also revealed new phenomena not researched by Dirlik, such as high vibration damage variation from stochastic loading, which depends on the frequency resolution (the block size used in Inverse Fourier Transformation). This discovery will be the subject of further study. Research results presented in this paper will also be utilised to combine stochastic and deterministic loading scenarios for military helicopters, as well as fighter aircraft, and will be the subject of further research.

Modeling of a Compact, Implantable, Dual-Band Antenna for Biomedical Applications

Different implantable antenna designs exist to establish communication with implantable devices depending on the domain of use and the implantation space. Owing to their nature and purposes, these antennas have many imposed criteria on various characteristics, such as bandwidth, multiband behavior, radiation pattern, gain, and specific absorption rate (SAR). This presents a challenge when it comes to achieving satisfying results without a major compromise in any of these crucial parameters. Additionally, many of the existing designs do not follow a specific approach to obtain results. Measuring different parameters of such fabricated structures requires special conditions and special environments mimicking the tissues where they are supposed to be placed. For such issues, the use of biological or synthetic phantoms is widely employed to validate what is obtained in simulation, and a multitude of formulas exist for the creation of such phantoms, each with its advantages and drawbacks. In this paper, a miniature dual-band structure derived from the first iteration of the Koch fractal structure is designed to operate 2 mm below the skin in the arm of the human body, with the MICS (Medical Implant Communication System) and ISM (Industrial, Scientific, Medical) 2.4 GHz bands. The purposes of the design are to derive structures from commonly used shapes with certain behavior while maintaining miniaturization, and to easily design dual-band implantable antennas. More than one band is used to diversify uses, since bands such as the MICS band are mainly dedicated to telemetry. The structure is characterized not only by its low profile compared to various structures found in the literature with dimensions of 17.2 × 14.8 × 0.254 mm3, but also its ease of design, independent shifting of resonant frequencies, and the absence of the need for a matching circuit and a shorting pin (via) for miniaturization. It exhibits satisfying performance: bandwidths of 23 MHz in the MICS band and 190 and 70 MHz in the vicinity of the ISM 2.4 GHz band, and measured gain in the latter band of −18.66 and −17 dBi in the azimuth and elevation radiation patterns, respectively. To validate the antenna’s properties in a skin-mimicking environment, two simple phantom formulas found in the literature were explored and compared in order to identify the best option in terms of accuracy and ease of fabrication.

Dimensionally-consistent equation discovery through probabilistic attribute grammars

Equation discovery, also known as symbolic regression, is a machine learning task of inducing closed-form equations from data and background knowledge. The latter takes various forms. Domain-specific knowledge can constrain the space of candidate equations to those that make sense in the scientific or engineering domain of use. Cross-domain knowledge, on the other hand, imposes general rules for model acceptability, such as parsimony, understandability, or consistency of the equations with the dimensional units of the variables. In this paper, we propose using attribute grammars to ensure the induced equations' dimensional consistency. Attribute grammars are flexible enough to combine cross-domain knowledge on dimensional consistency with domain-specific knowledge expressed as a probabilistic context-free grammar. At the same time, we show that attribute grammars can be efficiently transformed into probabilistic context-free grammars for equation discovery with existing algorithms. Finally, we provide empirical evidence that attribute grammars ensuring dimensional consistency of equations can significantly improve the performance of equation discovery on the standard set of a hundred Feynman benchmarks.