Examples Of Usage Research Articles

Advancing perspectives on the off-label use of anticancer drugs: an updated classification and exploration of categories.

To date, the definition that the off-label usage of drugs refers to the unapproved use of approved drugs, which covers unapproved indications, patient populations, doses, and/or routes of administration, has been in existence for many years. Currently, there is a limited frequency and prevalence of research on the off-label use of antineoplastic drugs, mainly due to incomplete definition and classification issues. It is time to embrace new categories for the off-label usage of anticancer drugs. This review provided an insight into an updated overview of the concept and categories of the off-label use of anticancer drugs, along with illustrating specific examples to establish the next studies about the extent of the off-label usage of anticancer drugs in the oncology setting. The scope of the off-label use of current anticancer drugs beyond the previous definitions not only includes off-label uses in terms of indications, patient populations, doses, and/or routes of administration but also off-label use in terms of medication course, combination, sequence of medication, clinical purpose, contraindications scenarios, etc. In addition, the definition of the off-label usage of anticancer drugs should be added to the condition at a given time, and it varies from approval authorities. We presented a new and relatively comprehensive classification, providing extensive analysis and illustrative examples of the off-label usage of antineoplastic drugs for the first time. Such a classification has the potential to promote practical adoption and enhance management strategies for the off-label use of antitumor drugs.

Illusion in Architecture: An Alternative for Creating Virtual Environment to Support Efficient Site Utilization

Virtual materiality has become a reality due to technological advancements. This affects the evolution of architecture. Land utilization in residential designs is currently unrestricted. Humans still exploit natural land and food supplies to build homes. Conversely, houses that merely use the land for viewing rather than traversing it are an example of inefficient land usage. In architecture, virtual materiality plays a part in creating an artificial world. In order to minimize land consumption, an architectural strategy that may introduce virtual materiality into the real world is required. Illusion is one tactic. Tricks and deceit are given priority in the illusion technique. This program is intended for home users who want to replace their natural surroundings with an artificial one. By using this method, nature can improve without requiring interference from the original environment. Modeling experiments and qualitative research are used in the research methodology. Create suggested concepts by using an architectural methodology. Rendering models and sketches are the research outputs. The findings demonstrate that home design can make use of illusion as a representation of virtual materiality. Through the application of matter illusion techniques, manufactured environments can be made to resemble natural ones. Implementing the material illusion in a trapezoidal home. Screen technology is used to do this. The center of the design is where screen technology is used to display environmental content. big horizontal and vertical sides are necessary. However, this can be a substitute for the natural environment for residents, especially in dwelling design.

Применение алгоритмов машинного обучения для сокращения информационной асимметрии и прогнозирования социальных изменений на рынке труда: вызовы и возможности

This paper explores the impact of machine learning (ML) algorithms on reducing information asymmetry in the labor market and forecasting social changes, presenting challenges and opportunities for employers and job seekers alike. It addresses the issues of information asymmetry stemming from disparities in data access between labor market participants, as well as variations in the quality of information obtained, complicating the hiring and job search processes. Theoretical foundations of big data and its analysis using ML are discussed for identifying trends and patterns conducive to more efficient labor market functioning. ML methods including predictive models, clustering, classification, and text analysis are presented, describing their application in reducing information asymmetry and adapting to changing market demands. Examples of ML algorithm usage in real business processes are provided, showcasing their contribution to optimizing recruitment and personnel management processes, as well as forecasting future trends regarding significant competencies and skills. The importance of adapting to new challenges and opportunities presented by ML algorithms for maintaining competitiveness and sustainable development in the labor market is emphasized. By leveraging big data analytics, employers and job seekers can make more informed decisions, leading to more efficient labor market outcomes. However, challenges such as data privacy and algorithmic bias must be addressed to fully realize the benefits of ML in the labor market.

A Secure Data Publishing and Access Service for Sensitive Data from Living Labs: Enabling Collaboration with External Researchers via Shareable Data

Intending to enable a broader collaboration with the scientific community while maintaining privacy of the data stored and generated in Living Labs, this paper presents the Shareable Data Publishing and Access Service for Living Labs, implemented within the framework of the H2020 VITALISE project. Building upon previous work, significant enhancements and improvements are presented in the architecture enabling Living Labs to securely publish collected data in an internal and isolated node for external use. External researchers can access a portal to discover and download shareable data versions (anonymised or synthetic data) derived from the data stored across different Living Labs that they can use to develop, test, and debug their processing scripts locally, adhering to legal and ethical data handling practices. Subsequently, they may request remote execution of the same algorithms against the real internal data in Living Lab nodes, comparing the outcomes with those obtained using shareable data. The paper details the architecture, data flows, technical details and validation of the service with real-world usage examples, demonstrating its efficacy in promoting data-driven research in digital health while preserving privacy. The presented service can be used as an intermediary between Living Labs and external researchers for secure data exchange and to accelerate research on data analytics paradigms in digital health, ensuring compliance with data protection laws.

O wartościujących etykietach językowych używanych względem małych dzieci

ON EVALUATIVE LINGUISTIC LABELS USED IN REGARD TO YOUNG CHILDREN The author of the article attempts to describe the linguistic phenomenon of labelling and stigmatization concerning children. In the introduction, the issue of child discrimination, known as adultism, is addressed. Then, the author discusses the origins and mechanisms of pejorative terms which frequently appear in online discussions related to motherhood and parenthood. In the analytical section, the author presents the most commonly used and negatively charged personal expressions, extracted,e. g. from the Monco PL search engine: bombelek, gówniak, kaszojad, purchlę and purchlak [pejorative terms denoting children]. Possible etymologies and usage examples are provided. Originally unequivocally negative terms for children also permeate colloquial language and gradually become neutralized or serve to distance oneself from parenthood. However, in this context, it’s worth referring to the ethics of language and promoting inclusive communication, which should also extend to talking about children and communicating with them.

PRINCIPLES OF COMPILING A GERMAN-UKRAINIAN DICTIONARY OF PARONYMS

This article addresses the principles of selecting paronyms and the methodology for compiling an electronic German-Ukrainian dictionary of paronyms using a corpus-based approach and systematic analysis to identify similarities and differences in the use of paronyms in speech. The author presents a concept of paronymy as a systemic phenomenon involving etymologically related words with similar morphemic structures but different meanings. By conducting a comparative analysis of collocations, the study identified 1,836 paronyms organized into paronymic series, each comprising two to nine components. The article examines existing dictionaries of paronyms and highlights their lexicographic shortcomings, such as excessive normative/prescriptive orientation, inadequate development of dictionary entries from both linguistic and didactic perspectives, and a lack of rigorous evaluations of modern data to inform dictionary compilation. The ideal paronym dictionary should rely on corpus data, providing reliable information with generalizations about individual word uses within conventionalized language usage. Corpus-based tools, such as those used for contrasting near-synonyms, systematically identify similarities and differences between paronyms in a contrastive manner. This process determines the degree of semantic closeness among expressions with similar contextual usage by comparing their immediate collocational patterns. The recorded semantic closeness or distance between paronyms is based on their contextual overlap in usage. The lexicographic processing of the selected paronyms was carried out with TshwaneLex, a computer-based dictionary creation software that allows lexicographers to develop dictionaries for any language without requiring advanced IT skills. TshwaneLex offers various features such as automatic cross-reference tracking, advanced dictionary comparison/merge functionality, and support for all world languages through full Unicode compatibility. The lemma editing process uses a tree-based interface to represent the hierarchical structure of the lemma, including different meanings, submeanings, word formations, multi-word units, examples of usage, cross-references, and more. Furthermore, the input/output architecture is designed to support the development of additional interfaces for various data sources as add-ons or plug-ins, facilitating other output formats. The article notes that a PHP-based software module is currently available for hosting the TshwaneLex dictionary on the internet, providing an accessible platform for electronic dictionary publication.

Category of learning modality in English texts

In the literature of linguistics, modality is a semantic category that expresses the attitude of the speaker to the content of his expression, the target designation of the speech, the relationship of the content of the expression with reality. The category of modality is a linguistic universal and stands out as one of the main categories of language. This article examines the category of modality, one of the most popular and interesting areas in modern English grammar. Different types of modality are defined, examples and features of working of modal words in English texts are given, detailed explanation is given to the presented usage examples. The relevance of the research is related to the necessity of a comprehensive review of the category of modality from the point of view of the theoretical grammar of the modern English language, the importance of systematizing modal words, and determining their status in linguistics.

GLC-Frame: A Framework and Library for Exploration of Multidimensional Data with General Line Coordinates

General Line Coordinates (GLC) are a relatively new set of line-based representations for visualizing multidimensional data with the distinctive characteristics of being reversible and lossless. Given these characteristics, the GLC have a high potential for exploratory multidimensional data analysis, however only partial implementations of some of the GLC techniques are available for the visualization community. In this paper, we present the GLC-Frame, an online exploration tool that supports a dual view and allows users to upload their own dataset and interactively explore the different GLC representations without writing code. We also present the GLC-Vis Library, an open-source data visualization library supporting GLC along with traditional interactions. Finally, we provide a set of usage examples showing how the different techniques behave in both the occlusion and the cluster identification problem. In addition, we present the interactions on GLC representations using the cars dataset. Both the GLC-Frame and the GLC-Vis Library provide an exploration space that will allow the visualization community to use these new techniques and evaluate their potential.

The IDSM mass spectrometry extension: searching mass spectra using SPARQL.

The Integrated Database of Small Molecules (IDSM) integrates data from small-molecule datasets, making them accessible through the SPARQL query language. Its unique feature is the ability to search for compounds through SPARQL based on their molecular structure. We extended IDSM to enable mass spectra databases to be integrated and searched for based on mass spectrum similarity. As sources of mass spectra, we employed the MassBank of North America database and the In Silico Spectral Database of natural products. The extension is an integral part of IDSM, which is available at https://idsm.elixir-czech.cz. The manual and usage examples are available at https://idsm.elixir-czech.cz/docs/ms. The source codes of all IDSM parts are available under open-source licences at https://github.com/idsm-src.

Pyaging: a Python-based compendium of GPU-optimized aging clocks.

Aging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research. To address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans. pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.

An Innovative Framework for Automated Software Testing and Validation

The rising scale and the intricate nature of these systems make the verification & validation (V&V) of classifications that are automated more costly and to much time-taking. Furthermore, the V and V of the following systems might be hampered if the techniques and procedures are not effectively documented, assessed, and selected. Practitioners must employ appropriate V and V methodologies and conduct acceptable V and V procedures to ensure the following systems perform as proposed and in cost-effective way. Earlier efforts have produced several categorizations and models that may be used to categorize V and V approaches and technologies. The goal of this study is to give a wide, comprehensive, and simple framework for characterization needs, rather than focusing on particular parts of V and V methodologies and procedures. To that purpose, in this study, we provide a multi-domained and multi-dimensioned framework for characterizing and classifying V and V approaches and tools in an organized manner. The framework reflects a complete description of several key features of V&V. As an example of usage, a web-based repository has been created based upon the framework to gather information regarding the use of V and V approaches and tackles. Experts and scholars may readily learn about and select acceptable V and V procedures this way.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

Richen: Automated enrichment of Git documentation with usage examples and scenarios

AbstractAs the predominant modern version control system, Git has become an indispensable tool for both commercial and open‐source software projects. It substantially improves software development effectiveness and efficiency through its distributed version control system, fostering seamless collaboration among teams and across locations. However, research has found that many developers have doubts about using Git commands, while the official Git documentation is rather scanty, that is, lacking sufficient explanations and examples. To help developers learn and use Git commands, we propose the first approach (Richen) for enriching Git documentation with usage examples and scenarios by leveraging crowd knowledge from Stack Overflow. Richen retrieves Git‐related posts from Stack Overflow, extracts relevant Q&A pairs, and selects representative command usages, including usage examples and scenarios, for different Git commands. Experimental results have shown that Richen can extract informative and concise command usages for Git commands. Compared with alternative methods adapted from API usage mining, the command usages obtained by Richen have significant advantages in terms of relevance, readability, and usability. Furthermore, we have shown through an empirical study that the command usages extracted by Richen can better help developers complete Git command‐related tasks.

A systematic review of wearable biosensor usage in immersive virtual reality experiences

Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.

PSSFSS—An Open-source Code for Analysis of Polarization and Frequency Selective Surfaces

The open-source code PSSFSS for analysis and design of polarization selective surfaces (PSSs), and frequency selective surfaces (FSSs) is presented, beginning with an introduction to the Julia programming language in which the code is written. Analysis methods and algorithms used in PSSFSS are described, highlighting features of Julia that make it attractive for developing this type of application. Usage examples illustrate the code’s ease of use, speed, and accuracy.

Crop-GPA: an integrated platform of crop gene-phenotype associations

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA (https://crop-gpa.aielab.net), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.

LEVERAGING CLOUD TECHNOLOGIES AND SERVERLESS ARCHITECTURE FOR EFFICIENT WEB DEVELOPMENT: A CASE STUDY FROM REAL-WORLD APPLICATION

This article provides a review of modern cloud solutions and serverless architecture using Backend as a Service (BaaS) and Function as a Service (FaaS) architecture as an example. In the scope of the article, a parallel is drawn between the consistently growing computing power and cloud technologies' growing popularity and availability for business. The results of an analytical review include a list of the most popular cloud providers from leading corporations and a comparison of low-level and high-level cloud technologies. The advantages and disadvantages of Google Cloud Platform (GCP) and Google Firebase are presented, where GCP is a low-level cloud provider and Firebase is a high-level cloud provider. The importance of understanding the context and specifics of the project when choosing cloud project solutions is emphasized which helps to maintain a balance between flexibility and development efficiency. The study introduces the practical utilization of the Supabase cloud platform for the development of a modern web application. The article convincingly proves the actuality of using Supabase for the development of an information system to optimize the modern personnel recruitment process, indicating specific advantages. An example of Supabase Edge Functions usage to generate feedback using the OpenAI Completions API and the Deno software platform is presented. The article convincingly proves that the use of cloud technologies is a modern strategy for building flexible, efficient, and scalable information systems. The advantages of the usage of the provision of infrastructure provided by world industry leaders are summarized.

Mancha3D Code: Multipurpose Advanced Nonideal MHD Code for High-Resolution Simulations in Astrophysics

The Mancha3D code is a versatile tool for numerical simulations of magnetohydrodynamic (MHD) processes in solar/stellar atmospheres. The code includes nonideal physics derived from plasma partial ionization, a realistic equation of state and radiative transfer, which allows performing high-quality realistic simulations of magnetoconvection, as well as idealized simulations of particular processes, such as wave propagation, instabilities or energetic events. The paper summarizes the equations and methods used in the Mancha3D (Multifluid (-purpose -physics -dimensional) Advanced Non-ideal MHD Code for High resolution simulations in Astrophysics 3D) code. It also describes its numerical stability and parallel performance and efficiency. The code is based on a finite difference discretization and a memory-saving Runge–Kutta (RK) scheme. It handles nonideal effects through super-time-stepping and Hall diffusion schemes, and takes into account thermal conduction by solving an additional hyperbolic equation for the heat flux. The code is easily configurable to perform different kinds of simulations. Several examples of the code usage are given. It is demonstrated that splitting variables into equilibrium and perturbation parts is essential for simulations of wave propagation in a static background. A perfectly matched layer (PML) boundary condition built into the code greatly facilitates a nonreflective open boundary implementation. Spatial filtering is an important numerical remedy to eliminate grid-size perturbations enhancing the code stability. Parallel performance analysis reveals that the code is strongly memory bound, which is a natural consequence of the numerical techniques used, such as split variables and PML boundary conditions. Both strong and weak scalings show adequate performance up to several thousands of processors (CPUs).

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool.

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters.

Illuminating the dark corners: a qualitative examination of cryptocurrency’s risk

PurposeThe study provides a comprehensive understanding of the issues and illegal activities related to cryptocurrencies and their negative repercussions. This study aims to identify and classify cryptocurrency downsides using grounded theory and in-depth interviews. The study also analysed investors’ reluctance to invest in cryptocurrency. This pioneering qualitative study illuminates a deep and multifaceted criminal aspect of cryptocurrency.Design/methodology/approachThe study conducted in-depth interviews with respondents who have experience and knowledge of cryptocurrency investments. The interviews were recorded and transcribed. The analysis was performed using the NVivo 14 software in the study.FindingsThe study specified two major types of cryptocurrency’s negative aspects: barriers and illegal usage. Barriers to cryptocurrency investment include technological, security, trust, market-related and regulatory reasons. Terrorist funding, money laundering, fraud and ransom payments are all examples of illegal usage. The results of the word cloud analysis are consistent with the overall findings of the survey, which highlighted illegal usage as a prominent negative element of cryptocurrencies. It is a key reason why cryptocurrency is not included in investing portfolios by investors.Originality/valueThe study’s findings provide useful insights for policymakers to develop better methods for successfully mitigating risks and ensuring responsible and sustainable usage of cryptocurrencies. In addition, the study could serve as a stepping stone for more cryptocurrency-related studies, contributing to the development of a more complete and nuanced comprehension of this emergent technology and its societal effects.