Automated Data Analysis Research Articles

Mapping sidewalk accessibility with smartphone imagery and Visual AI: a participatory approach.

Evaluating sidewalk accessibility is conventionally a manual and time-consuming task that requires specialized personnel. While recent developments in Visual AI have paved the way for automating data analysis, the lack of sidewalk accessibility datasets remains a significant challenge. This study presents the design and validation of Sidewalk AI Scanner, a web app that enables quick, crowdsourced and low-cost sidewalk mapping. The app enables a participatory approach to data collection through imagery captured using smartphone cameras. Subsequently, dedicated algorithms automatically identify sidewalk features such as width, obstacles or pavement conditions. Though not a replacement for high-resolution sensing methods, this method leverages data crowdsourcing as a strategy to produce a highly scalable, city-level dataset of sidewalk accessibility, offering a novel perspective on the city's inclusivity; fostering community empowerment and participatory planning.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Chemical Screening, Identification, and Comparison of Tripterygium Hypoglaucum Hutch Preparations by Ultra-High-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry Combined With Multivariate Statistical Analysis.

Colquhounia root tablets (CRT) and Tripterygium hypoglaucum hutch tablets (THHT), two major Tripterygium hypoglaucum hutch (THH) commercial preparations, have been used to treat chronic kidney diseases or rheumatic diseases. However, there have been no reports on the chemical comparison between CRT and THHT, greatly hindering the understanding of their pharmacological difference as well as their rational application in clinical practice. In the present study, ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry combined with automated data analysis by MS-DIAL software and MS-FLO website was employed to systematically screen and characterize the components in CRT and THHT. Multivariate statistical analysis was used to compare the differences between these two preparations. As a result, up to 92 components were tentatively identified, and 17 of them were characterized for the first time in THH preparations. According to the criteria of variable importance in projection (VIP) >1, p < 0.05, and fold change (FC) > 1.2, 46 components could be screened as major differential chemical components. Among them, phenolic acids, organic acids, amino acids, and diterpenoids were higher in CRT, while the sesquiterpene alkaloids were relatively higher in THHT. This study clarified the chemical material basis and the difference between CRT and THHT, providing a valuable reference for quality control and clinical rational use of THH preparations.

Imperial Study on Convolutional Layer in CNN for Data Mining

Convolutional Neural Networks (CNNs) have become a cornerstone in the field of data mining, particularly for tasks involving large-scale image and pattern recognition. This paper presents an imperial study on the efficacy of convolutional layers within CNN architectures in data mining applications. We analyze the impact of varying convolutional layer configurations, such as kernel size, stride, and depth, on performance metrics including accuracy, computational cost, and feature extraction quality. By conducting a series of experiments across different datasets, we explore how these configurations influence the model's ability to generalize and detect complex patterns in structured and unstructured data. Our findings indicate that optimizing the convolutional layers significantly improves the efficiency of CNNs for data mining tasks, offering insights into best practices for architectural design in such applications. This study provides valuable guidelines for leveraging CNNs in the realm of data mining, pushing the boundaries of automated data analysis and knowledge discovery

Methodology for extracting narratives from social media big data

Purpose: of the article is to present the experience in developing and testing the methodology for extracting a system of narratives on a socially significant phenomenon from authentic social network big data (using the example of narratives about COVID-19 vaccination in the Russian social network VKontakte during the pandemic).Methods: of automated data analysis were used by the tools of the PolyAnalyst analytical platform: topic modeling (PLSA method), text indexing algorithms with the sentence identification stage, clustering, data aggregation, data normalization, calculation of a quantitative index. The calculation of the measure of proximity of keywords using the Python, partial manual markup and data validation were also carried out.Results: 4.5 million messages relevant to the topic of COVID-19 vaccination published in VKontakte from 01.01.2020 to 01.03.2023 were reduced to 237 stable narratives. A popularity index was calculated for each narrative. For example, the following narrative turned out to be the most popular: “Employers put pressure on people to get vaccinated” (it was supported by 76,118 texts). As a result of the study, a dataset was obtained, including 237 narratives.Conclusions and Relevance: the developed toolkit is universal: the methodology can be adapted to any relevant topic, requiring only adjustments to the input parameters of thematic modeling. The obtained dataset is planned to be introduced into scientific circulation as an up-to-date material for studying public opinion on vaccination in Russia. The results contribute to international research on public opinion and communication in crises and can serve as a basis for practical actions aimed at improving the quality of public communications and decision-making at all levels of government.

The ECP ALPINE project: In situ and post hoc visualization infrastructure and analysis capabilities for exascale

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent.

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

AUTOMATION OF DATA ANALYSIS USING ARTIFICIAL INTELLIGENCE METHODS

The article discusses modern approaches to automating data analysis using artificial intelligence (AI) methods. With the rapid growth of data volumes entering various systems, their analysis and processing are becoming a complex task. Automating these processes with AI allows us to increase the efficiency and accuracy of data analysis, minimize the human factor, and speed up decision-making. The article discusses machine learning and deep learning methods used to automate data analysis, as well as examples of their application in various industries, such as finance, medicine, industry, and marketing. Particular attention is paid to the advantages and limitations of existing approaches, as well as prospects for their further development. The article discusses in detail the conditions and methods of research aimed at studying and evaluating the effectiveness of various AI models in automating data analysis. The obtained results are analyzed and prospects for further development of AI technologies in this area are discussed. The study emphasizes the importance of interpretability of AI models, the need to develop new methods that can effectively work with limited and noisy data, as well as reducing the computational costs associated with their use.

Twenty years of volcano data at GeoNet—collection, custodianship, and evolution of open data on New Zealand’s volcanoes

The GeoNet programme at GNS Science has monitored and managed data for volcanoes, earthquakes, landslides, and tsunami in Aotearoa New Zealand since 2001. Volcano monitoring data are collected from seismometers, acoustic sensors, GNSS receivers, webcams, remote gas monitoring sensors, and a range of environmental sensors, as well as manually during visits to volcanoes. The primary user of volcano data is the internal cross-specialised Volcano Monitoring Group (VMG), which fulfils the role of the national volcano observatory. GeoNet concentrates on automatic data collection and analysis, while supporting members of the VMG with manual data collection and interpretation. The application of open-data principles to both data and metadata has always been a core aspect of GeoNet; responses have been overwhelmingly positive, despite concerns regarding some high value, manually collected datasets. The website www.geonet.org.nz represents the primary data access portal. Data analysis and delivery applications are organised by data type rather than hazard, with no volcano-specific data applications. Most datasets have web-based and API delivery application options; both provide standard data formats from a cloud-based archive. One of the challenges for volcano data collection and management has been shifting from a reliance on manually collected data to automatic collection. Additionally, awareness of important questions related to Indigenous Māori data governance is increasing, although the associated impact is not yet understood. Overall, the current centralised, cooperative volcano monitoring and data collection and management system, which benefits from improved efficiency, interoperability, and data quality, has proved effective in Aotearoa New Zealand. Ongoing work aims to ensure optimal data collection and management for volcano monitoring and downstream activities.

In situ Synchrotron X-ray Metrology Boosted by Automated Data Analysis for Real-time Monitoring of Cathode Calcination.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

Research on the Application of AI Technology in Auditing

With the rapid development of AI technology, the application in the field of auditing has become an important means to improve the efficiency and quality of auditing. AI technology has significantly improved the efficiency and quality of audit work by automating data analysis, risk assessment, and audit processes. However, as technology evolves, so do challenges such as data privacy, security, algorithmic transparency, and lack of talent. In order to address these issues, it is recommended to strengthen data protection, improve the accuracy and transparency of algorithms, formulate unified AI audit standards, and focus on cultivating audit talents with AI skills. In the future, the audit industry needs to actively manage and control risks while enjoying the convenience brought by AI, ensure the quality and credibility of audit work, and promote the development of audit services in a more efficient and intelligent direction.

Generating interpretable rainfall-runoff models automatically from data

A sudden surge of data has created new challenges in water management, spanning quality control, assimilation, and analysis. Few approaches are available to integrate growing volumes of data into interpretable results. Process-based hydrologic models have not been designed to consume large amounts of data. Alternatively, new machine learning tools can automate data analysis and forecasting, but their lack of interpretability and reliance on very large data sets limits the discovery of insights and may impact trust. To address this gap, we present a new approach, which seeks to strike a middle ground between process-, and data-based modeling. The contribution of this work is an automated and scalable methodology that discovers differential equations and latent state estimations within hydrologic systems using only rainfall and runoff measurements. We show how this enables automated tools to learn interpretable models of 6 to 18 parameters solely from measurements. We apply this approach to nearly 400 stream gaging sites across the US, showing how complex catchment dynamics can be reconstructed solely from rainfall and runoff measurements. We also show how the approach discovers surrogate models that can replicate the dynamics of a much more complex process-based model, but at a fraction of the computational complexity. We discuss how the resulting representation of watershed dynamics provides insight and computational efficiency to enable automated predictions across large sensor networks.

The impact of gender on the nursing figure and nurses' interprofessional relationships: A multimethod study.

To identify the current presence of stereotypes about the nursing profession in Italy and to understand how gendered processes and modalities are regulated and expressed in the physician-nurse dyad, and the implications for professional identity and autonomy. Qualitative multimethod design. Forty-five interviews were conducted with nurses and physicians. The collected qualitative data underwent automatic textual data analysis using a multidimensional exploratory approach and a gender framework analysis. In Italy, nurses' roles are still associated with gender stereotypes stemming from the predominant male culture, which affects sexual and gender identity, the division of labor, and access to career paths. This leads to disadvantages in the nursing profession, which is heavily dominated by women. Biological differences between sexes generate an unconscious yet shared symbolic gender order composed of negative stereotypes that influence nurses' professional roles and activities. They follow behaviors that enter the work routine and institutionalize organizational processes. These effects are also seen in the asymmetric, limited, and reciprocal interprofessional relationships between male physicians and female nurses, where the former hinders the latter's professional autonomy and access to top positions. This survey raises awareness of gender issues and stimulates reflection. It also enables health and nursing organizations to take action to raise gender awareness and education by countering the image of a non-autonomous profession. The analysis of gender processes allows us to identify interventions that can counteract forms of oppression in the work environment that lead to the emergence of nursing as a non-autonomous profession.

Lean Management and use of AI in healthcare: living in the present

This study explores the possible relationship between Lean Management and generative artificial intelligence (Generative AI), which are seemingly distant but with common objectives namely to reduce waste and increase efficiency. Our analysis reveals that Generative AI models can support “lean” principles by facilitating just-in-time manufacturing, increasing flexibility, promoting waste reduction, and rapid prototyping. However, solutions are needed for the technological challenges and the quality of the content generated. Effective implementation requires a focus on optimizing operations and automating data analysis, as well as aligning goals between stakeholders. The joint use of cross-functional teams can maximize resource utilization and drive informed decisions. Generative AI is therefore a powerful complement to Lean Management in business processes. A focus was then placed on the impact that AI and technological advancement may have in various areas of the pharmaceutical sector. The state of the art was assessed by referring to publications in the literature and by interviewing experts not only with a corporate profile, but also community pharmacists, students, and professionals in the pharmaceutical sector.

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Chi-Squared Automatic Interaction Detection Decision Tree Analysis of Social Determinants for Low Birth Weight in Virginia.

This study provides additional context to the literature regarding the social inequities that impact birth outcomes in Virginia using a decision tree analysis. Chi-squared automatic interaction detection data analysis (CHAID) was performed using data from the Virginia birth registry for the years 2015-2019. Birth weight was the outcome variable, while sociodemographic factors and maternity care deserts were the explanatory variables. The prevalence of low birth weight in Virginia was of 8.1%. The CHAID decision tree model demonstrated multilevel interaction among risk factors with three levels, with a total of 34 nodes. All the variables reached significance in the model, with race/ethnicity being the first major predictor variable, each category of race and ethnicity having different significant predictors, followed by prenatal care and maternal education in the next levels. These findings signify modifiable risk factors for low birth weight, in prioritizing efforts such as programs and policies. CHAID decision tree analysis provides an effective approach to detect target populations for further intervention as pathways derived from this decision tree shed light on the different predictors of high-risk population in each of the race/ethnicity demographic categories in Virginia.

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

The double‐edged sword of generative artificial intelligence in digitalization: An affordances and constraints perspective

AbstractGenerative artificial intelligence (AI) has gained prominence across various industries and domains, offering capabilities to generate human‐like text, creative ideas, and solutions. This paper explores customers' responses to the use of generative AI in digitalizing content production and consumption processes. Drawing on technology affordance theory, this article examines how are the affordances of generative AI leveraged to contribute to the gradual digitalization of individuals. This netnographic study is based on over 9 months naturalistic observations of the AI Community online, culminating in 1572 pages of data. The findings identify different types of affordances that foster digitalization: automated content creation, automated data analysis, and AI‐generated content dissemination. This study also identifies the constraints of generative AI and discusses potential interventions to address these constraints and prevent unintended consequences. This research provides insights for scholars, professionals, and educators to better understand the dynamics of leveraging generative AI.

AI-Driven Structural Health Monitoring: Unleashing Potential and Embracing Future Opportunities

Structural Health Monitoring (SHM) is a critical aspect of ensuring the safety and longevity of infrastructure such as bridges, buildings, and dams. Traditional SHM techniques, while effective, often rely on manual inspections and can be laborintensive, time-consuming, and prone to human error. Recent advancements in Artificial Intelligence (AI) have the potential to revolutionize SHM by automating data analysis, improving predictive capabilities, and enhancing overall system efficiency. This article explores the integration of AI in SHM, highlighting key innovations, challenges, and future directions for this transformative technology.

Finding the Fingerprint of an PEMWE Electrolyzer Using Applied Machine Learning Techniques

The energy transition is picking up pace and renewable energy sources are playing an increasing role in our energy supply. The use of green hydrogen is considered an important energy carrier, to store renewable energy and allow many industries to decarbonize. There are different ways to produce hydrogen, whereas low-temperature proton exchange membrane water electrolysis (PEMWE) is one of the more mature electrolysis technologies. The benefits of proton exchange membrane water electrolysis (PEMWE) include the ability to deliver pressurized hydrogen and/or oxygen, operate using intermittent and renewable power sources, and have a reduced footprint. To evaluate the characteristic performance of PEMWE, the voltage/current relation is an important metric. The voltage/current relation is commonly represented in jV-curves. jV-curves contain relevant information, and any issues or out-of-specification performance can be observed immediately. The shape of the curve, and indicated values allow to interpret the performance of the PEWME cell. Nevertheless, proper interpretation of jV-curves and understanding of the source leading to the out-of-specification performance requires expertise.This study aims to evaluate jV-curves and identify faults by applying machine learning (ML) techniques. A conventional approach has been used to create the model. In the model the Nernst voltage, activation voltage on the anode, and the voltage losses due to ohmic resistance are modeled. Implementation of optimization algorithms allow to fit measured jV-curve to the model. Literature has shown relevant techniques to apply these kinds of ML techniques to electrolysis data, for example Particle Swarm Optimization and a modified Honer Badger algorithm. The application of ML to electrolysis data enables the opportunity to develop online monitoring and automated interpretation of data. In this study particle Swarm Optimization (PSO) has been implemented to fit the model to the jV-curve and find the unknown parameters. Those parameters are unique, depending on the performance of the electrochemical cell, and can be interpreted at the fingerprint thereof.The data shown in the figure has been measured on a single-cell material screener using Nafion 117 CCMs. Although both CCMs have been produced in the same batch, a difference between the measurements can be observed. The jV-curve of sample 1 shows performance as expected. This expected performance is characterized by its linear behavior above 0.5 A/cm2. Observing sample 2 shows an increase in voltage at increasing current densities compared to sample 1. This increase in voltage results in increased energy consumption and therefore a less efficient electrolysis process. Both CCMs are measured under comparable conditions. The anode and cathode pressure were during the both tests close to atmospheric, and the average cell temperature was kept constant at 333.15 K.Despite high volume production processes for coating and manufacturing, the origin of the difference in performance remains unknown. The application of automated data analysis allows for the immediate evaluation of the parameters at fault. The preliminary results show a discrepancy in the modeling parameters relevant to the sub-model describing ohmic losses. Furthermore, ML allows for the creation of data sets that contain experiences from the past. This can help to develop faster performance evaluation protocols by reducing testing activities. For future work, alternative algorithms have to be considered as well. Utilizing ML can enable the opportunity for online monitoring, predictive maintenance, and optimizing control strategies. Figure 1

Ultra-dense nodal seismic acquisition for automated geohazard analysis

In this paper, we address the problem of shallow geohazard mapping by developing an integrated workflow consisting of nodal seismic acquisition and automated data analysis. Densely spaced point-receiver land nodes and point sources are adopted as an alternative to receiver arrays or groups conventionally used in seismic exploration. The recently developed methodology of surface-consistent decomposition of the transmitted wavefield is applied to the nodal data. Ultra-resolution mapping of surface-consistent attributes in amplitude and phase is obtained, together with the generation of high-energy signal beams at the common midpoint positions, forming virtual (shot) supergathers (VSGs). The VSG preserves frequency and true-amplitude content thanks to the surface-consistent corrections applied before beam forming. Traveltimes, waveforms, and surface waves are then analyzed in the high signal-to-noise ratio VSG, leading to inversion of the subsurface parameters. Human-intensive interpretation of surface-wave dispersion curves in the f-k spectra is facilitated by the introduction of efficient machine learning algorithms for the unsupervised and supervised paradigms. The ultra-dense nodal acquisition and automated data analysis are effectively applied to the analysis of several cases of near-surface geohazards.