Digital Analysis Research Articles

Utilizing digital footprint analysis for end-to-end risk-based authentication in medical billing systems

Healthcare systems face escalating challenges in securing sensitive financial and patient data due to sophisticated fraud tactics and unauthorized access. This study presents a dynamic risk-based authentication (RBA) framework that leverages digital footprint analysis, including behavior monitoring, device recognition, and location-based anomaly detection, to strengthen security. The framework utilizes machine learning models, Isolation Forest for outlier detection, and recurrent neural networks for sequential behavior analysis, to assess risk in real-time, dynamically adjusting authentication requirements based on risk profiles. Privacy-preserving technologies such as homomorphic encryption and federated learning are integrated to comply with HIPAA and GDPR standards, ensuring secure data handling without centralization. Findings show that the proposed RBA framework effectively reduces false positives, improves detection accuracy, and provides a scalable solution for securing medical billing systems. This adaptive approach supports both user experience and stringent privacy compliance, laying the groundwork for more resilient healthcare data security systems. Future studies could extend this framework by incorporating blockchain to enhance data transparency and auditability across transactions.

Respiratory Disease Detection using Convolutional Neural Networks and Audio Signal Analysis

This study investigates the application of respiratory audio signals for the detection of lung disorders, such as asthma, COPD, and pneumonia, utilizing convolutional neural networks (CNNs). Lung disorders frequently generate atypical noises such as wheezes, crackles, and stridor as a result of tracheal and pulmonary problems. Although manual inspection formerly enabled early detection, the intricacy of contemporary diseases demands more accurate diagnostic instruments. This research establishes a system that analyzes audio data, extracts features, and utilizes convolutional neural networks to categorize pulmonary disorders based on respiration sounds. Advanced methodologies such as digital stethoscopes and frequency modulation analysis are employed to segment and scrutinize lung sounds, facilitating the identification of respiratory abnormalities. The technology utilizes machine learning and AI to provide a non-invasive, economical method for identifying conditions such as pneumonia and interstitial lung disease. Despite limitations including hearing interference and data harmonization, this technology possesses significant potential to revolutionize early detection and remote monitoring of lung disorders, enhancing healthcare outcomes in both clinical and resource-limited environments.

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

Optimizing Basil Seed Vigor Evaluations: An Automatic Approach Using Computer Vision-Based Technique

The short cultivation cycle and high essential oil content of basil plants render them a valuable raw material source for diverse industries. However, large-scale production is hindered by the lack of specific protocols to assess seed vigor; thus, a consistent supply of high-quality seeds that meet consumer demands cannot be ensured. This study investigated the effectiveness of an automated system for seedling analysis as a tool for evaluating basil seed vigor and compared it to traditional tests. For this purpose, seeds from eight commercial lots were evaluated in two separate trials spaced six months apart using the following tests: germination, first germination count, saturated salt accelerated aging, primary root emergence, mean germination time, seedling emergence, seedling emergence speed index, and computerized seedling image analysis. The parameters provided by the system allowed us to clearly and objectively classify the basil seed lots based on vigor, and the results were strongly and significantly correlated with the findings of traditional vigor tests, particularly between the vigor index and seedling length. Digital analysis of four-day-old seedlings proved to be a fast and efficient technique for evaluating basil seed vigor and has the potential for use in automating the data collection and analysis process.

Diachronic and historical evolution of the Al-Hoceima Bay coastline, Moroccan Mediterranean Sea

Al-Hoceima Bay, located on the northern coast of Morocco, holds significant environmental importance. It also faces environmental challenges, including the pressures resulting from urban sprawl and growing number of tourists, as well as the impacts of climate change. The objective of this study is to assess the coastal changes in Al- Hoceima Bay since 1964, considering both natural and human factors. This study is based on the diachronic analysis of aerial photographs taken over a period of 56 years, utilising the digital shoreline analysis system statistical technique to calculate the shoreline’s mobility index for each period. The results demonstrate significant erosion at the Tayth beach (−1.50 m∙y−1) and Souani beach (approximately −1.90 m∙y−1), whereas accretion was experienced at the Sfiha beach at a rate of about +1.11 m∙y−1 and at the Lharch beach at a rate of +0.92 m∙y−1. The mouth of the Nekôr River experienced the highest retreat at −3.15 m∙y−1, followed by Ghiss at −2.00 m∙y−1. These findings indicate the impact of human interventions, such as the construction of the Mohamed Ben Abdelkarim El Khattabi dam on Oued Nekôr since 1981, as well as climate changes that have led to decreased flow, particularly at Oued Ghiss. These combined climatic and anthropogenic impacts have exacerbated erosion and disrupted the sediment balance along the shoreline of Al-Hoceima Bay.

Sensitive and reliable detection of KIT p.D816V mutation in decalcified archival bone marrow trephines.

The majority of mastocytosis cases are characterized by an activating mutation in the KIT gene in codon 816. The detection of this alteration is of importance for proper diagnostic workup. Therefore, reliable and sensitive methods for the detection of KIT Codon 816 hotspot mutations in various types of patient samples are required. Since mutated cancer genes are often overexpressed, we evaluated the feasibility and sensitivity of KIT p.D816V detection by analysing mRNA/cDNA instead of genomic DNA. From 80 bone marrow trephines harboring a KIT p.D816 mutation, seven were only mutated by mRNA/cDNA pyrosequencing and 11 only by digital PCR analysis of genomic DNA. These results clearly demonstrate that detection of clinically relevant mutations in mRNA extracted from routinely processed decalcified archival bone marrow trephines is not only possible in a reliable fashion but under many circumstances advantageous. This enables the direct correlation of genomic data with high-quality morphological evaluation.

Salda-Yeşilova Survey: The Necropolis of Takina in the Context of Digital Epigraphy and New Inscriptions Found During the Survey

This article presents the epigraphic results of surveys carried out in the Yeşilova district of Burdur province as part of the Salda – Yeşilova Surveys in 2022. In this context, the evaluation focuses on old and new epigraphic materials discovered in the centre of Yeşilova, particularly in the village of Yarışlı. This study presents a digital and photogrammetric analysis of the research carried out in the village of Yarışlı since the 1800s by various researchers, including J. Arundell, A. H. Smith, W. M Ramsay, G. E. Bean, G. Labarre, M. Özsait, and N. Özsait. The focus is on the necropolis of Takina and Yeşilova. The inscriptions on chamosorions and sarcophagi in the Takina necropolis have been scanned using the photogrammetric method in order to check their legibility. After the traditional methods of reading, the images obtained from the sarcophagi scanned by technological methods were compared with the results of previous researchers. As a result, two inscriptions in the Takina necropolis were enhanced and corrected. In addition, two new inscriptions were discovered in the area, one of which is well preserved. Outside this area, three new inscriptions were discovered, one a fragment of a tomb, one a table and crater offering to the god Men, and one an offering to Dionysus.

Abstract 4137989: Notched P-wave by digital ECG analysis was associated with new-onset atrial fibrillation onset and ischemic stroke

Background: A bimodal P wave in lead II reflects left atrial remodeling and is described as a notched P wave. A notched P wave is usually defined as a dip over the smallest unit of electrocardiography (ECG) recording paper, 40 ms, but automated analysis of ECGs has shown that even a notch of 20 ms is associated with cardiovascular events. A notched P wave is also known to predict atrial fibrillation (AF) after catheter ablation. However, the relationship between automatically assessed notched P waves and new-onset AF and ischemic stroke in patients without documented AF has not been clarified. Hypothesis: A notched P-wave by digital ECG analysis was associated with both new-onset AF and ischemic stroke. Methods: We enrolled 4,216 subjects from the Cardiovascular Prognostic Coupling Study in Japan (Coupling Registry) who had one or more cardiovascular risk factors. Twelve-lead electrocardiography was conducted, and the peak-to-peak distance in the M shape was calculated automatically using a 12-lead ECG analysis system. We compared two definitions: P-waves defined as “notched” at the the peak-to-peak (“M shape”) distance in lead II of ≥20 ms or ≥40 ms. New-onset AF was confirmed through routine medical care as well as annual ECGs. We defined the primary endpoint as new-onset AF and the secondary endpoint as ischemic stroke. Results: The mean follow-up period was 53 ± 17 months, during which 17 AF cases developed. When a notched P-wave was defined as ≥20 ms (n = 319), it was a significant predictor of both new-onset AF (hazard ratio [HR] 2.91, 95% confidence interval [CI] 1.41–6.01, p=0.004) and ischemic stroke (HR 3.02, 95% CI 1.56–5.83, P=0.001). A notched P-wave defined as ≥40 ms (n = 63) was also a predictor of new-onset AF (HR 4.57; 95% CI:1.42–14.71, p = 0.011) and ischemic stroke (HR 3.84, 95% CI 1.20–12.29, P=0.024). Conclusions: A notched P-wave by digital ECG analysis was associated with ischemic stroke as well as the new-onset AF.

A Digital Engineering Analysis of an Additively-Manufactured Turbine Vane

Abstract Additive manufacturing (AM) has transformed the ability to accelerate gas turbine component research and development at a fraction of the cost and time associated with conventional manufacturing. However, whereas prior works have assessed manufacturing variability in cast turbine airfoils, limited data are available to understand the impact of as-built deviations in AM turbine parts. As metal additive airfoils are becoming more prevalent in research turbine architectures, it is increasingly important to understand the effects of potential hardware deviations specific to additively-manufactured parts. With this goal in mind, the current study utilizes a digital engineering approach to evaluate the aerodynamic impact of surface deviations on a high-pressure turbine vane design created for research purposes. Reynolds-averaged Navier–Stokes-based computational fluid dynamics studies derived from structured light scans of as-built turbine vanes are used to quantify performance relative to design-intent geometries. Further computational analyses compare results from individual serialized parts with an average vane doublet geometry serving as a surrogate for the entire wheel. Particular emphasis in the study focuses on external surface defects caused by internal cooling features that are inherent through additive manufacturing and how these features can impact the vane performance. Ultimately, this study identifies specific regions of the vane that are subject to increased sensitivity, which benefits future designers intending to use AM as a tool for turbine research and development.

Differential Glial Response and Neurodegenerative Patterns in CA1, CA3, and DG Hippocampal Regions of 5XFAD Mice

In this study, the distinct patterns of glial response and neurodegeneration within the CA1, CA3, and dentate gyrus (DG) regions of the hippocampus were examined in 5XFAD mice at 6 and 12 months of age. The primary feature of this transgenic mouse model is the rapid onset of amyloid pathology. We employed quantitative assessments via immunohistochemistry, incorporating double staining techniques, followed by observation with light microscopy and subsequent digital analysis of microscopic images. We identified significantly increased Aβ deposition in these three hippocampal regions at 6 and 12 months of transgenic mice. Moreover, the CA1 and CA3 regions showed higher vulnerability, with signs of reactive astrogliosis such as increased astrocyte density and elevated GFAP expression. Additionally, we observed a significant rise in microglia density, along with elevated inflammatory markers (TNFα) in these hippocampal regions. These findings highlight a non-uniform glial and neuronal response to Aβ plaque deposition within the hippocampal regions of 5xFAD mice, potentially contributing to the neurodegenerative and memory deficit characteristics of Alzheimer’s disease in this model.

Clinical values of nuclear morphometric analysis in fibroepithelial lesions.

Fibroepithelial lesions (FELs) of the breast encompass a broad spectrum of lesions, ranging from commonly encountered fibroadenomas (FAs) to rare phyllodes tumors (PTs). Accurately diagnosing and grading these lesions is crucial for making management decisions, but it can be challenging due to their overlapping features and the subjective nature of histological assessment. Here, we evaluated the role of digital nuclear morphometric analysis in FEL diagnosis and prognosis. A digital nuclear morphometric analysis was conducted on 241 PTs and 59 FAs. Immunohistochemical staining for cytokeratin and Leukocyte common antigen (LCA) was used to exclude non-stromal components, and nuclear area, perimeters, calipers, circularity, and eccentricity in the stromal cells were quantified with QuPath software. The correlations of these features with FEL diagnosis and prognosis was assessed. All nuclear features, including area, perimeter, circularity, maximum caliper, minimum caliper and eccentricity, showed significant differences between FAs and benign PTs (p ≤ 0.002). Only nuclear area, perimeter, minimum caliper and eccentricity correlated significantly with PT grading (p ≤ 0.022). For differentiation of FAs from benign PTs, the model integrating all differential nuclear features demonstrated a specificity of 90% and sensitivity of 70%. For PT grading, the nuclear morphometric score showed a specificity of 78% and sensitivity of 96% for distinguishing benign/borderline from malignant PTs. In addition, a relationship of nuclear circularity was found with PT recurrence. The Kaplan-meier analysis, using the best cutoff determined by ROC curve, showed shorter event free survival in benign PTs with high circularity (chi-square = 4.650, p = 0.031). Our data suggested the digital nuclear morphometric analysis could have potentials to objectively differentiate different FELs and predict PT outcome. These findings could provide the evidence-based data to support the development of deep-learning based algorithm on nuclear morphometrics in FEL diagnosis.

The Effect of Digital Analytics Capacity and Open Eco-Innovation on The Reduction And Waste Of Grain Losses

Objective: The study aims to analyze the effect of digital capacity and open process and management eco-innovation on the reduction and disposal of grain losses. Theoretical Framework: In this topic, digital analysis capacity and open eco-innovation are presented, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprises a quantitative approach, with grain companies in Brazil. Data was collected through a questionnaire, in which 175 responses were obtained and analyzed using structural equation modeling (PLS-SEM). Results/Discussion: The results obtained revealed that open-process eco-innovation helps in the prevention and disposal of grain losses. In contrast, open management eco-innovation helps in the disposal of grain losses. Furthermore, open process and management eco-innovation mediate the relationship between digital analysis capability and the allocation of grain losses. Research Implications: Practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in subject management and sustainability. Originality/Value: This study contributes to the literature on open process and management eco-innovation as an intermediary mechanism to explore the relationship between digital analysis capability and the prevention and allocation of grain losses. The relevance and value of this research are evidenced by showing managers strategies and ways to prevent grain losses that occur in the production, post-harvest, and processing phases.

Defeating the Dark Sides of FinTech: A Regression-Based Analysis of Digitalization’s Role in Fostering Consumers’ Financial Inclusion in Central and Eastern Europe

Financial technologies metamorphose economies with customer-focused innovation. In this way, financial inclusion is fostered and economic growth is increased. However, risks, trust issues, and ethical concerns stem from the faster advancement of digital technologies and expanding financial innovation. Thus, this paper aims to understand the risks and barriers associated with FinTech and consumer adoption, focussing on the impact of digitalization on financial products/services’ acceptance. The research investigates the impact of digitalization on financial services and the recognition of the role played in the global economy by FinTech. For this reason, the regression analysis was used to explore the influence and correlation of various variables on FinTech in Central and Eastern European (CEE) countries, such as Internet usage, online shopping, paying bills via the Internet, and making and receiving digital payments. The results show differences between three clusters of CEEs in terms of FinTech adoption. While several past studies have explored the advantages of FinTech, few studies have investigated the risks associated with its adoption, trust, and barriers to its usage in different country contexts. The present paper fills the gap by analysing the data on Internet usage, online shopping, paying bills via Internet, and sending or receiving digital payments in CEE countries. The study recommends that FinTech companies share information online not only to present their offerings to users, but also to promote financial education through clear and straightforward communication about the features of their services. This approach can indirectly benefit society by contributing to financial development, inclusion, social stability, and, consequently, sustainable development.

Geo-spatial analyses of meandering rivers, assessing past and future impacts on bank landforms and LULC changes

ABSTRACT Morphological investigation of rivers is difficult but is one of the vital topics for the management of highly meandering river systems. The study employs GIS to analyse 32 years of Barak River data, addressing challenges in quantifying the morphology of meandering rivers. Earth observatory data from 1990 to 2022 track channel bank shifts and Land Use/Land Cover (LULC) alterations. Although the digital shoreline analysis system (DSAS) model identified long-term bank line migration analysis of shorelines, it's use is very new for rivers, especially in terms of the meandering of rivers. Conversely, research on long-term meandering river morphology in the Barak River channel has not been conducted despite evident land-use changes. Using DSAS for bank positions and CA–Markov models for LULC , future projections until 2043 reveal ongoing Barak River bank shifts and significant LULC pattern alterations. The findings of the research were validated through RMSE, Student's t-test, chi-square, and the kappa coefficient. Forecasts indicate that rapid urbanization impacts river morphology. The methodology is crucial for managing highly meandering rivers, particularly in contexts involving vulnerable river systems.

Retracing digital assumptions in social movements: the Italian case of No Tav

This paper aims to shed light on the key role of digital repertoire in shaping participation forms, challenging internet as a flexible, dynamic, and pervasive platform that transcends reality in terms of sense and relationship construction processes. The research design traces the narration and performance patterns in the last decade of the No Tav movement case study. The work examines the concept of innovation as both an evolving repertoire and a set of users’ practices. The empirical analysis is conducted through digital content analysis using quantitative textual analysis techniques. The research questions aim to understand how activists produce and share knowledge, what mobilization patterns emerge and are enabled by digital media, and how organizational assumptions change due to the digital turn.

Digital Cephalometric Analysis: Unveiling the Role and Reliability of Semi-automated OneCeph, Artificial Intelligence-Powered WebCeph Mobile App, and Semi-automated Computer-Aided NemoCeph Software in Orthodontic Practice

Digital Cephalometric Analysis: Unveiling the Role and Reliability of Semi-automated OneCeph, Artificial Intelligence-Powered WebCeph Mobile App, and Semi-automated Computer-Aided NemoCeph Software in Orthodontic Practice

Multiscale evaluation of sand-geomaterial interface shear response in terms of material geometry effects

This study evaluates the effects of particle and surface geometric features on the interface shear behavior of sand-geomaterial. Particle shape and size were adjusted for the sand, and surface roughness form and height were set for the geomaterial part. 3D printing technology was used to produce geomaterials in the desired form. A custom-made transparent interface shear box was developed to perform digital image processing. For macroscale response, peak and residual strengths and dilation angle were measured. Sliding percentage and shear band thickness were determined from digital image correlation analyses for the mesoscale response. The experimental results showed that the macroscale response parameters increased as the overall regularity of the sand particles decreased. The increase in mean particle size and roughness height led to an increase in macroscale parameters. Digital image correlation analyses showed that the sliding percentage had an inverse correlation with the shear band thickness in terms of the particle shape and the roughness height effects. The roughness form controlled the trend of mean particle size effects on the sliding percentage. Overall, the mesoscale parameters revealed the traces of interlocking response, particle kinematics, and the failure mechanism in the interface region. Thus, a bridge was established between meso- and macro-scale responses.

“Shock kidney-like Appearance”: Objective evaluation of renal color changes in hemorrhagic shock deaths

Severe bleeding due to various traumatic injuries can cause hemorrhagic shock, which is difficult to diagnose using forensic medicine. Therefore, we defined the difference in color between the renal cortex and medulla observed in hemorrhagic shock deaths as “shock kidney-like appearance (SKLA)” and digitally analyzed the color difference with a digital camera and color analysis software. The aim of this study was to develop and evaluate a method for objectively determining SKLA and improve the accuracy of forensic diagnosis. We examined the kidneys of 122 cases (83 males and 39 females; average age, 64.8 years) autopsied at our facility. Using Image J, we analyzed the color of the cortex and medulla from photographs of bisected kidneys. We defined the color difference between the cortex and medulla in the L*a*b* color space as cortical-medullary color difference and performed a comparative analysis between the hemorrhage and control groups. Significant differences were observed in ΔL* and Δa* values between the two groups (p < 0.05 and p < 0.001, respectively). Analysis of Δa* values showed that the cortex was less reddish than the medulla in the hemorrhage group. The cutoff value for determining SKLA was set at Δa* = −1.33 (sensitivity, 0.79; specificity, 0.81; AUC, 0.859). Traditional evaluations of color rely on subjective assessments, which raise issues of reliability and reproducibility. This study successfully overcame the limitations of subjective evaluation by objectively assessing cortical-medullary color difference in the kidneys. Our results represent an important step towards improving the objectivity of color evaluations.

Correlations Between Mandibular Kinematics and Electromyography During the Masticatory Cycle: An Observational Study by Digital Analysis

The analysis of the masticatory cycle plays a fundamental role in studying the functions of the stomatognathic system and evaluating temporomandibular dysfunctions (TMD). The primary objective of this study is to investigate the complex interplay between mandibular kinematics and surface electromyography (sEMG) activity during the masticatory cycle using advanced 4D dentistry technology in 22 healthy subjects (without TMD). By employing electromyography, it becomes feasible to capture the electrical activity of the masticatory muscles throughout the chewing process. The BTS TMJOINT (© 2023 BTS Bioengineering, Garbagnate Milanese, MI, Italy) electromyograph was utilized in this study. Mandibular tracking, on the other hand, allows for recording the movements of the mandible during chewing and condylar slopes. This latest technology (ModJaw®, Tech in motion™, Villeurbanne, France) utilizes motion sensors placed on the jaw to accurately track three-dimensional movements, including jaw opening, closing, and lateral movements. Nowadays, in clinical gnathology, it is common practice to examine masticatory function by analyzing mandibular kinematics and muscle contraction as distinct entities. Similarly, the results obtained from these analyses are typically assessed independently. The investigation of a correlation between electromyography data and mandibular kinematics during the masticatory cycle could provide several advantages for clinicians in diagnosis and lead to a combined analysis of muscle activities and intraarticular dynamics. In conclusion, it can be inferred from the results obtained in the present study that the chewing cycle with a greater vertical movement results in increased masseter muscular activity, and condylar slopes are positively correlated to an increase in temporalis muscle activation. This comprehensive approach can provide valuable insights into the relationship between muscle activity and mandibular movement, enabling clinicians to gain a deeper understanding of the functional dynamics of the stomatognathic system.

Insights for Managers About AI Adoption in New Product Development

OVERVIEW: The artificial intelligence (AI) revolution in new product development (NPD) has reached a critical juncture, demanding decisive action from business leaders. Our comprehensive study of key AI applications in NPD reveals a startling lack of adoption across industries, particularly in the United States. Despite the potential benefits, businesses also demonstrate weak intentions to adopt AI and low readiness regarding AI integration into NPD processes. Despite the low adoption rate, the study’s results offer a compelling picture of AI’s potential, including substantial positive impacts of AI use in key areas like product design, prototyping, testing, digital twin simulations, market analysis, and new product ideation. Benefits include accelerated development and markedly improved decision-making.