Data Analysis Tools Research Articles

INTEGRATION OF DIGITAL TECHNOLOGIES INTO THE PERSONNEL DEVELOPMENT MANAGEMENT MECHANISM IN HEALTHCARE INSTITUTIONS

This article explores the integration of digital technologies into the personnel management mechanisms of healthcare institutions in Ukraine. In an era marked by rapid digital transformation, the healthcare sector is increasingly recognizing the importance of utilizing modern technological tools to enhance operational efficiency, improve patient care, and streamline administrative processes. The study begins by examining the current state of personnel management in Ukrainian healthcare institutions, identifying key challenges such as workforce shortages, inefficient communication channels, and inadequate training programs. The research further delves into the various digital solutions available, including human resource management systems (HRMS), telemedicine platforms, and data analytics tools. These technologies are evaluated for their potential to optimize recruitment processes, enhance employee performance tracking, and facilitate continuous professional development. The article also highlights case studies from healthcare institutions that have successfully implemented digital technologies, showcasing tangible improvements in staff engagement, patient outcomes, and overall institutional effectiveness. Additionally, the paper discusses the critical role of digital literacy among healthcare professionals, emphasizing the need for targeted training initiatives to ensure that staff can effectively utilize new technologies. It concludes by proposing a strategic framework for integrating digital tools into personnel management practices, advocating for a holistic approach that encompasses technological adoption, staff training, and organizational culture change. Ultimately, the article aims to provide valuable insights and practical recommendations for healthcare leaders seeking to navigate the complexities of digital integration in personnel management. It has been determined that further research can significantly contribute to the development of effective personnel management strategies in healthcare institutions, which will not only enhance the quality of medical services but also promote the sustainable development of the healthcare system in Ukraine.

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification

Abstract Background Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88–91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.

WebTWAS 2.0: update platform for identifying complex disease susceptibility genes through transcriptome-wide association study

Abstract Transcriptome-wide association study (TWAS) has successfully identified numerous complex disease susceptibility genes in the post-genome-wide association study (GWAS) era. Over the past 3 years, the focus of TWAS algorithms has shifted from merely identifying associations to understanding how single nucleotide polymorphisms (SNPs) regulate gene expression, with a growing emphasis on incorporating fine-mapping techniques. Additionally, the rapid increase in GWAS summary statistics, driven largely by the UK Biobank and other consortia, has made it essential to update our webTWAS resource. To address these challenges and meet the growing needs of researchers, we developed webTWAS 2.0, an updated platform for identifying susceptibility genes for human complex diseases using TWAS. Additionally, webTWAS 2.0 provides an online TWAS analysis tool that simplifies conducting TWAS analyses. The updated resource includes 7247 GWAS summary statistics covering 1588 complex human diseases from 192 publications. It also incorporates multiple TWAS methods, such as sTF-TWAS, 3′aTWAS and GIFT, along with an updated interactive visualization tool that allows users to easily explore significant associations across different methods. Other upgrades include a personalized online analysis tool for user-submitted GWAS data and a refined search function that makes it easier to identify relevant associations and meet diverse user needs more efficiently. webTWAS 2.0 is freely accessible at http://www.webtwas.net.

The Impact of Information Technology On Data-Driven Decision Making In Human Resource Management

In the contemporary landscape of human resource management (HRM), the integration of information technology (IT) has revolutionized data-driven decision-making processes. This shift is characterized by the increased reliance on data analytics, cloud computing, and advanced software applications that enable HR professionals to harness vast amounts of data effectively. As organizations strive for agility and competitiveness, the role of IT in HRM has become pivotal, influencing various aspects such as recruitment, performance management, employee engagement, and talent development. The first major impact of IT on data-driven decision-making in HRM is the enhancement of recruitment processes. Traditional hiring methods often relied on subjective assessments and limited candidate data. However, IT solutions, such as Applicant Tracking Systems (ATS) and AI-driven recruitment tools, have enabled HR managers to analyze candidates’ qualifications, experiences, and fit with organizational culture based on data-driven metrics. This not only improves the quality of hires but also reduces time-to-fill positions and enhances overall recruitment efficiency. Performance management has evolved significantly due to the integration of IT. Performance appraisal systems now utilize real-time data analytics to track employee performance against key performance indicators (KPIs). This allows HR managers to make informed decisions about promotions, training needs, and workforce planning. The implementation of continuous feedback mechanisms, facilitated by IT platforms, fosters a culture of transparency and engagement, enabling employees to take ownership of their development. IT has transformed employee engagement strategies through the use of data analytics. Organizations now leverage employee surveys and feedback tools that provide insights into employee satisfaction and engagement levels. By analyzing this data, HR professionals can identify trends, address concerns proactively, and implement targeted initiatives that enhance workplace culture and retention rates. Such data-driven approaches ensure that decisions are aligned with employee needs and organizational objectives. The role of IT in talent development cannot be overstated. Learning Management Systems (LMS) and data analytics tools provide HR managers with the capability to assess skill gaps and monitor training effectiveness. By analyzing learning outcomes and employee progress, organizations can tailor development programs that align with both individual career paths and organizational goals. This strategic alignment is crucial for fostering a skilled workforce capable of meeting future challenges. Despite these benefits, the reliance on IT in HRM also poses challenges, particularly regarding data privacy and security. As organizations collect and analyze large volumes of employee data, they must navigate complex regulations and ethical considerations to protect sensitive information. This underscores the need for robust data governance frameworks that ensure compliance while fostering a culture of trust within the organization. In conclusion, the impact of information technology on data-driven decision-making in human resource management is profound. By leveraging data analytics and advanced IT tools, organizations can enhance their recruitment processes, improve performance management, boost employee engagement, and foster talent development.It is essential for HR professionals to address the challenges of data privacy and security to maximize the benefits of IT in HRM. As technology continues to evolve, the synergy between IT and HRM will play a critical role in shaping the future of work.

WEST is an ensemble method for spatial transcriptomics analysis.

Spatial transcriptomics is a groundbreaking technology, enabling simultaneous profiling of gene expression and spatial orientation within biological tissues. Yet when analyzing spatial transcriptomics data, effective integration of expression and spatial information poses considerable analytical challenges. Although many methods have been developed to address this issue, many are platform specific and lack the general applicability to analyze diverse datasets. In this article, we propose a method called the weighted ensemble method for spatial transcriptomics (WEST) that utilizes ensemble techniques to improve the performance and robustness of spatial transcriptomics data analytics. Wecompare the performance of WEST with six methods on both synthetic and real-world datasets. WEST represents a significant advance in detecting spatial domains, offering improved accuracy andflexibility compared to existing methods, making it a valuable tool for spatial transcriptomics data analytics.

Integrating FinOps in healthcare for optimized financial efficiency and enhanced care

The integration of Financial Operations (FinOps) in healthcare is increasingly recognized as a critical strategy for optimizing financial efficiency and enhancing patient care. This paper explores the principles and practices of FinOps, emphasizing its role in improving cost management, fostering financial transparency, and ensuring better resource allocation in healthcare organizations. By addressing traditional inefficiencies in healthcare financial management, FinOps promotes collaboration between finance and operations, enabling real-time financial decision-making that aligns with both operational goals and patient outcomes. Additionally, the paper outlines the challenges healthcare organizations face in managing rising costs and complex billing systems, and provides practical strategies for the successful implementation of FinOps, including the use of advanced technologies such as cloud-based platforms and data analytics tools. Ultimately, the adoption of FinOps not only enhances short-term financial efficiency but also contributes to the long-term sustainability and scalability of healthcare services.

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review.

There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making. The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation. A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years. Nine articles that presented 7 data analytics tools designed to inform policy making for NCDs were reviewed. The tools incorporated descriptive and predictive analytics. Some tools were designed to include recommendations for decision support, but no pilot studies applying prescriptive analytics have been published. The tools were piloted with various conditions, with cancer being the least studied condition. Implementation of the tools included use cases, pilots, or evaluation workshops that involved policy makers. However, our findings demonstrate very limited real-world use of analytics by policy makers, which is in line with previous studies. Despite the availability of tools designed for different purposes and conditions, data analytics is not widely used to support policy making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy making. Based on the findings, we make suggestions for researchers developing digital tools to support public health policy making. The findings will also serve as input for the European Union-funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

Admission Prediction for Freshmen Students Using Low-cost Data Analytics Tool

This research paper tackles the admission prediction of enrollment for the tertiary level using the Naïve Bayes classification technique. Using data mining, the Naïve Bayes algorithm is simulated using Data Analytics Tool (DAT) and Weka data mining to provide the classifier model in evaluating if the given college freshmen applicant will continue or discontinue to be admitted to college. In line with the method, the researcher used gender, senior high school average grade, senior high school type, senior high school strand, and college entrance exam result. The training data set used for this machine learning model comprises 750 records from the different freshmen applications. As a result of the classifier model testing in Weka, the correct instances captured from the original 750 records is 86.27%, which is significantly higher and makes the classifier model reliable. In addition, a confusion matrix is performed and computed with an accuracy of 86.27%, precision of 83.13%, recall of 95.34%, and F1 score of 88.82%. Thus, the model test result is favorable to the reliability of the classifier model using Naïve Bayes technique.

Leveraging Data Analytics for Financial Stability: A Blueprint for Sustaining SMEs in Economically Distressed Regions of the U.S.

Purpose: Our research explores how data analytics can empower small- and medium-sized enterprises (SMEs) in economically distressed regions of the U.S. by improving financial decision-making, operational efficiency, and risk mitigation. The study aims to provide a blueprint for leveraging data-driven insights to ensure long-term financial stability and economic growth in underserved areas, particularly in the Midwest and Southern states. Methodology: The study employs a case-study approach to analyze the real-world applications of data analytics platforms in SMEs within economically distressed regions. It uses statistical analysis to compare the performance of businesses that have adopted data analytics tools with those relying on traditional methods. The methodology also includes the collection of data from various sources such as financial performance records, customer analytics, and predictive models. Findings: The research demonstrates that SMEs using data analytics experience significant improvements in financial performance. For instance, predictive analytics increased financial forecasting accuracy by 25%, and real-time business intelligence tools reduced operational costs by 15%. Businesses using customer analytics tools saw a 20% rise in customer retention and a 15% increase in revenue. The study shows that data-driven strategies can improve overall financial stability by 30% and enable SMEs to proactively mitigate risks in volatile markets. Unique Contribution to Theory, Policy, and Practice: The study contributes to the emerging body of literature on the transformative potential of data analytics in financially distressed regions, providing new insights into how real-time and predictive analytics can enhance financial decision-making in SMEs’ practices within the economically distressed regions. The study provides a practical blueprint for SMEs to adopt data analytics tools, demonstrating their impact on financial stability, operational efficiency, and long-term growth. It offers actionable recommendations for business leaders in distressed regions to integrate analytics equitably into their decision-making processes.

Standardised formats and open-source analysis tools for the MAGIC telescopes data

Instruments for gamma-ray astronomy at Very High Energies (E>100GeV) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted 166h of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy.

Cyberactivism and Social and Political Participation Regarding the War in Ukraine: Analysis of Social Interactions in Spanish on Twitter

To evaluate and understand the dynamics of citizen participation in social networks concerning the war in Ukraine and delve into the phenomenon of cyberactivism, this study focuses on the conversation generated in Spanish around the conflict on Twitter. The research analyses 1,138,747 original tweets to investigate the general characteristics of the conversation, the user interaction patterns, and the creation and structure of communities and to determine the connecting factors. The study employs machine learning and artificial intelligence techniques via the Graphext data analytics tool. The conversation volume is very high, but the network of interactions is characterized by being unstructured and dominated by white noise, with disjointed interactions setting the tone of the conversation. Verified accounts and emotionality stand out as factors of connection and interconnection in the network. This case study highlights a form of social and political participation oriented towards visibility and information about the cause at a level of deliberation and debate that shows empathy and sympathy in a context close to slacktivism.

Multi-criteria decision making beyond consistency: An alternative to AHP for real-world industrial problems

The Analytic Hierarchy Process (AHP) is a widely used method for multi-criteria decision-making that hinges on the assumption of consistency in pairwise comparisons. However, in real-world scenarios, decision-makers often struggle to provide fully consistent judgments. This article introduces a decision-making framework that operates independently of consistency. Utilizing the Skew-Symmetric Bilinear representation of preferences allows decision-makers to more accurately evaluate alternatives and criteria, making this framework more applicable in practical settings. The proposed method is validated through practical examples and an in-depth case study in the textile industry, effectively resolving a complex decision-making problem related to acquiring a data analytics tool for supplier selection. The results underscore the robustness and adaptability of this consistency-independent technique as a flexible alternative to traditional AHP methods.

YOLO-CIANNA: Galaxy detection with deep learning in radio data

Context. The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. Aims. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. Methods. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Results. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40–60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10–30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512×512 pixels per second on a single GPU. Conclusions. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors.

HYBRID CLOUD DATABASES FOR BIG DATA ANALYTICS: A REVIEW OF ARCHITECTURE, PERFORMANCE, AND COST EFFICIENCY

The increasing reliance on big data analytics has driven organizations to seek more flexible, scalable, and cost-effective data management solutions, with hybrid cloud databases emerging as a prominent option. Hybrid cloud databases integrate both public and private cloud environments, allowing businesses to balance the scalability and cost advantages of public clouds with the security and control provided by private clouds. This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, examines the architecture, performance, cost efficiency, security, and technological advancements of hybrid cloud databases. The study synthesizes findings from 40 peer-reviewed articles published between 2015 and 2024, focusing on key factors that affect the performance of hybrid cloud databases, such as workload distribution, network latency, and the use of advanced data analytics tools like Hadoop and Spark. The review also explores cost-saving mechanisms, including dynamic resource scaling and pay-as-you-go pricing models, which help organizations reduce infrastructure costs by up to 28%. Additionally, it discusses the security risks and privacy concerns associated with hybrid cloud environments, highlighting the effectiveness of encryption protocols and identity access management systems in mitigating data breaches by as much as 40%. Furthermore, the review identifies emerging trends in the integration of machine learning and artificial intelligence (AI), which have significantly enhanced system automation and resource optimization in hybrid cloud infrastructures, leading to performance improvements of up to 45%.

Challenges to Public Administration Related to the Implementation of the Scientific and Technological Development Strategy of the Russian Federation

The author considers the requirements of the Decree of the President of Russia “On the Scientific and Technological Development Strategy of the Russian Federation” to the characteristics and principles of functioning of the public administration system in Russia. It is shown that the list of these characteristics and requirements is incomplete and insufficient for the implementation of the Decree. The main gaps in the requirements formed by the Decree relate to limiting the reconfiguration of the management system to only one subsystem, management in the field of scientific and technological development. The article argues that the complex nature of grand scientific and technological challenges implies profound changes in the public administration system as a whole, the transition from a structural and functional management system (a combination of meritocratic and client-oriented models) to a project management system (indicative and public-state models). The changes should affect the nomenclature and the scheme of interaction between structural divisions of state bodies, increase the level of project intra- and interdepartmental coordination. In addition, significant changes must be made in the public service system. We should organize training of specialists capable of using data analytics tools and data-based decision-making technologies as well as of carrying out an indicative assessment of project implementation indicators. The structure of the civil service should be associated with project offices and temporary performance of duties in these offices, and not only with permanent functions, taking into account the specifics of project service activities and project teams within government agencies. Civil servants should actively interact with independent expert structures and with civil society, which is the ultimate beneficiary of science and technology projects.

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

River morphology information system: A web cyberinfrastructure for advancing river morphology research

The study of river systems is challenged by the complexity and volume of data required to understand and predict river morphology changes. The River Morphology Information System (RIMORPHIS) addresses these challenges with an open-access web-based cyberinfrastructure for advanced river morphology research. Built on the National Hydrography Dataset Plus High Resolution, RIMORPHIS integrates publicly available bathymetry data and third-party resources into a cohesive database architecture for real-time use and analysis. The platform, structured around a PostgreSQL database with PostGIS extension, offers tools for geospatial visualization using Deck.GL, data analytics with Python-based geospatial processing, and eHydro Cross Section Surveys integration via API. By providing on-demand access to relevant datasets and tools for data analytics and geospatial visualization, RIMORPHIS enhances data accessibility and interoperability, supporting informed decision-making in river basin management. This paper presents the development and functionalities of RIMORPHIS and its contributions to hydrological information systems as a self-sustained community platform.

Nonparametric Multiple-Output Center-Outward Quantile Regression

Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content, the graphs of which constitute nested center-outward quantile regression tubes with given unconditional probability content; these (conditional and unconditional) probability contents do not depend on the underlying distribution—an essential property of quantile concepts. Empirical counterparts of these concepts are constructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistently reconstruct (in the Pompeiu-Hausdorff topology) their population versions. Our method is entirely nonparametric and performs well in simulations—with possible heteroscedasticity and nonlinear trends. Its potential as a data-analytic tool is illustrated on some real datasets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.