Analysis Tools Research Articles

Sliding-window cross-correlation and mutual information methods in the analysis of solar wind measurements

Context. When describing the relationships between two data sets, four crucial aspects must be considered, namely: timescales, intrinsic lags, linear relationships, and non-linear relationships. We present a tool that combines these four aspects and visualizes the underlying structure where two data sets are highly related. The basic mathematical methods used here are cross-correlation and mutual information (MI) analyses. As an example, we applied these methods to a set of two-month’s worth of solar wind density and total magnetic field strength data. Aims. Two neighboring solar wind parcels may have undergone different heating and acceleration processes and may even originate from different source regions. However, they may share very similar properties, which would effectively “hide” their different origins. When this hidden information is mixed with noise, describing the relationships between two solar wind parameters becomes challenging. Time lag effects and non-linear relationships between solar wind parameters are often overlooked, while simple time-lag-free linear relationships are sometimes insufficient to describe the complex processes in space physics. Thus, we propose this tool to analyze the monotonic (or linear) and non-monotonic (or non-linear) relationships between a pair of solar wind parameters within a certain time period, taking into consideration the effects of different timescales and possible time lags. Methods. Our tool consists of two parts: the sliding-window cross-correlation (SWCC) method and sliding-window mutual information (SWMI) method. As their names suggest, both parts involve a set of sliding windows. By independently sliding these windows along the time axis of the two time series, this technique can assess the correlation coefficient (and mutual information) between any two windowed data sets with any time lags. Visualizing the obtained results enables us to identify structures where two time series are highly correlated, while providing information on the relevant timescales and time lags. Results. We applied our proposed tool to solar wind density and total magnetic field strength data. Structures with distinct timescales were identified. Our tool also detected the presence of short-term anti-correlations coexisting with long-term positive correlations between solar wind density and magnetic field strength. Some non-monotonic relationships were also found. Conclusions. The visual products of our tool (the SWCC+SWMI maps) represent an innovative extension of traditional numerical methods, offering users a more intuitive perspective on the data. The SWCC and SWMI methods can be used to identify time periods where one parameter has a strong influence on the other. Of course, they can also be applied to other data, such as multi-wavelength photometric and spectroscopic time series, thus providing a new tool for solar physics analyses.

Learning Aligned Vertex Convolutional Networks for Graph Classification.

Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To address this problem, in this article, we propose a family of aligned vertex convolutional network (AVCN) models that focus on learning multiscale features from local-level vertices for graph classification. This is done by adopting a transitive vertex alignment algorithm to transform arbitrary-sized graphs into fixed-size grid structures. Furthermore, we define a new aligned vertex convolution operation that can effectively learn multiscale vertex characteristics by gradually aggregating local-level neighboring aligned vertices residing on the original grid structures into a new packed aligned vertex. With the new vertex convolution operation to hand, we propose two architectures for the AVCN models to extract different hierarchical multiscale vertex feature representations for graph classification. We show that the proposed models can avoid iteratively propagating redundant information between specific neighboring vertices, restricting the notorious oversmoothing problem arising in most spatial-based GCN models. Experimental evaluations on benchmark datasets demonstrate the effectiveness.

Agile fusion: developing 'Eat at Right Place' sentiment analysis tool

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

Функционально-трудоемкостный анализ кадровых процессов системы менеджмента качества организации

The article discusses an approach to improving personnel (supporting) processes in the organisation’s quality management system based on the methodology tool modification of the functional-cost analysis (FCA) and the qualimetric substantiation of critical indicators of the studied process function parameters. The paper analyses tools and technologies for structuring, correcting, and improving personnel processes in the organization’s quality management system as well as problems associated with their application in practice. The expediency for operational determination of the direction for improving the personnel process quality is substantiated by studying a limited number of their parameters. Importance (significance) and labour intensity of personnel process functions are considered as the studied parameters. A procedure for functional and labour-intensive analysis (FLIA) is proposed, which is the personnel process improvement based on analysing the importance-labour intensity ratio of function implementation, allowing the process owner to quickly obtain information about the process state. The procedure of functional and labour-intensive analysis is considered through the approbation on the example of the personnel process “Personnel Recruitment.” Critical indicators of “Personnel Recruitment” structure are compared with similar indicators obtained based on applying functional-cost analysis to the process under consideration. The article argues for the promising use of the proposed procedure for enhancing the personnel processes of the organization’s quality management system, aimed at identifying the direction for their improvement.

Impact of internal pre-damage due to drying shrinkage under various humidity conditions on DEF expansion in cement paste and mortar

This study explores the impact of internal pre-damage from early drying shrinkage on the delayed ettringite formation (DEF) expansion behavior of cement-based materials subjected to high-temperature processes. Specimens of cement paste and mortar with water-cement (W/C) ratios of 0.4, 0.5, and 0.6 were characterized after curing under relative humidity (RH) conditions of 50, 70, and 100 for 56 days. The parameters assessed were drying shrinkage, dynamic elastic modulus, and free water content. Subsequent DEF expansion over time was quantified, and the underlying mechanism was analyzed using XRD and SEM techniques after 270 days of underwater curing. Results indicate that: (1) a lack of adequate early curing humidity significantly reduces the dynamic elastic modulus with more than a 10% drop in cement paste following 56 days at RH50; (2) a higher W/C ratio and lower early curing humidity lead to more internal damage due to shrinkage, thereby promoting DEF expansion; (3) drying intensifies internal pre-damage in the interfacial transition zone (ITZ), causing an earlier deterioration of DEF expansion in mortar compared to cement paste. However, the presence of aggregates constrains this, leading to lesser DEF expansion in mortar; (4) dynamic elastic modulus changes as measured by ultrasonic methods can quantitatively characterize the extent of deterioration caused by DEF expansion, thus offering a tool for non-destructive testing and DEF expansion deterioration analysis.

Misuse of XPS in Analyzing Solid Polymer Electrolytes for Lithium Batteries

One of the most powerful spectroscopic tools for battery analysis is X-ray photoelectron spectroscopy (XPS); however, its great power, must be accompanied by great responsibility for authenticity. Fluorine is documented to be unstable under XPS conditions, and fluorinated salts used in Li batteries show photodecomposition. As all-solid-state batteries advance, demand for surface characterization is increasing. Here, a popular solid polymer electrolyte comprising a fluorinated salt in a PEO matrix was measured by XPS. Rapid photodecomposition after few minutes produced mainly LiF, initially not found on the surface. Not being aware of such artifacts may lead to an erroneous analysis of the characterized electrochemical system.

Abstract 7426: The GenePattern ecosystem for cancer bioinformatics

Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The GenePattern ecosystem, first introduced in 2004 and updated continually to support the changing needs of cancer genomics research, supports the analytical, computational, and reproducibility needs of the world-wide cancer analysis community.The GenePattern server, available at www.genepattern.org, provides hundreds of analysis methods for scientists at all levels of computational sophistication, with the only requirement for use being a web browser. The GenePattern server offers bulk and single-cell RNA-seq, copy number variation, flow cytometry, network analysis, general machine learning, gene set enrichment analysis, proteomics, and many other modalities. Analysis steps can be linked together into pipelines that can then be edited, shared, and made public. All parameters of an analysis, including the code version, are recorded so that an analytical result can be reproduced at any point in the future.The GenePattern Notebook system, notebook.genepattern.org, is an integration of the Jupyter Notebook environment with the GenePattern server. It combines the research narrative capabilities of Jupyter with the non-programming approach and breadth of analyses available in GenePattern. Scientists using GenePattern Notebook can create documents that include richly-formatted text and multimedia, executable code, and GenePattern analyses. A single GenePattern notebook can therefore comprise a sophisticated analytical workflow that runs on multiple remote servers.We have recently expanded GenePattern Notebook into a new notebook environment, Genomics to Notebook, g2nb.org, which adds the ability to include analyses on any public Galaxy server as well as using the Integrative Genomics Viewer (IGV) and Cytoscape within notebooks. Each analysis or visualization appears a cell within a notebook, preserving the accessibility and ease of use of the notebook metaphor while retaining the essential aspects of each tool’s user interface. When run, the entire analysis appears to execute seamlessly within the notebook. The online workspace also includes a library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. Citation Format: Michael M. Reich, Thorin Tabor, John Liefeld, Edwin Huang, Forrest Kim, Jill P. Mesirov. The GenePattern ecosystem for cancer bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7426.

Abstract 920: Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research

Abstract Data sharing plays a crucial role in accelerating biomedical research discoveries, promoting rigorous and reproducible research, enabling research data reuse, and fostering public trust. To facilitate data sharing, a coordinated, sustainable, and streamlined infrastructure is required to store, preserve, retrieve, and make data findable, accessible, interoperable, and reusable. Starting January 25, 2023, researchers funded by the National Institutes of Health (NIH) are required to comply with the NIH Data Management and Sharing Policy (https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview) and indicate how research data funded by the NIH will be managed and broadly shared. As institutions prepare to meet these requirements, the National Cancer Institute (NCI) is looking to understand the existing processes of data storage, preservation, and sharing including what infrastructure and services are available to facilitate data sharing. To carry out this goal, the NCI’s Office of Data Sharing (ODS), dedicated to promoting responsible and efficient sharing of cancer research data, issued a Request for Information (RFI) to understand the existing data sharing processes for NIH-Funded Research. The RFI was designed to collect information on services (e.g. guidance on data standards and file formats), technologies (e.g. storage, compression, retrieval, archival, analysis tools, workbenches), and processes (e.g. governance, communication, education, outreach) that promote data sharing. ODS utilized the RFI to gather input from stakeholders involved in facilitating data sharing. This included personnel from various fields such as research laboratories, scientific instrumentation core facilities, offices of research and/or sponsored programs, provost offices or other research administration, libraries, information technology, and institutional review boards. We also engaged with bioinformaticians and data scientists who are actively contributing to data curation, formatting, and analysis. Here, we present analysis of the responses received, including insights relating to gaps and challenges in data sharing, and proposed next steps and recommendations. Citation Format: Mousumi Ghosh, Nathan Boyd, Subhashini Jagu. Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 920.

A Universal and Programmable Platform based on Fluorescent Peptide-Conjugated Probes for Detection of Proteins in Organelles of Living Cells.

Realizing protein analysis in organelles of living cells is of great significance for developing diagnostic and therapeutic methods of diseases. Fluorescent-labeled antibodies with well imaging performance and high affinity are classical biochemical tools for protein analysis, while due to the inability to effectively enter into cells, not to mention organelles and the uncontrollable reaction sites that might cause antibodies inactivation when chemically modification, they are hard to apply to living cells. Inspired by the structure of fluorescent-labeled antibodies, we designed as a universal detection platform that was based on the peptide-conjugated probes (PCPs) and consisted of three parts: a) a rotor type fluorescent molecular scaffold for conjugation and signal output; b) the cell penetration protein recognition unit; c) the subcellular organelle targeting unit. In living cells, PCPs could firstly localize at organelles and then proceed protein specific recognition, thus jointly leading to the restriction of twisted intramolecular charge transfer and activation of fluorescence signal. As a proof-of-concept, six different proteins in three typical intracellular organelles could be detected by our platform through simply replacing the recognition sequence of proteins and matching organelle targeting units. The position and intensity of fluorescence signals demonstrated specificity of PCPs and universality of the platform.

Water Energy Food Nexus in Urbanizing Wetlands: Challenges and Solutions Explored through Choremes and Focus Maps

Addressing complex interactions within water, energy, and food (WEF) resources, innovative tools for in-depth analysis and decision-making are imperative. This study introduces chorematic focus maps (CFMs) as a groundbreaking method to visualize and tackle the WEF nexus’s complexities, focusing specifically on the Danube Delta Biosphere Reserve (DDBR). By merging geospatial analysis with on-site validation, this research reveals intricate interdependencies within the nexus and positions CFMs as an effective tool for stakeholders. This study adopts a methodological approach that focuses on identifying human activities and evaluating their impacts on the WEF nexus, with the goal of developing practical and grounded strategies for managing these essential resources. By testing this approach within the DDBR, the potential for wider application is demonstrated, offering a promising framework for addressing similar socio-environmental challenges across various regions. Future research directions include refining CFMs’ precision and practicality through extended fieldwork and stakeholder engagement, testing the framework’s adaptability across various locations and nexus dynamics. Additionally, incorporating cutting-edge technologies such as machine learning could provide deeper insights and reinforce CFMs’ role in decision support for the WEF nexus. Conclusively, this investigation into the WEF nexus through CFMs emphasizes the critical need for strategies that navigate the complexities of environmental management and resource optimization, marking CFMs as a significant tool for both decision-makers and researchers.

A Universal and Programmable Platform based on Fluorescent Peptide‐Conjugated Probes for Detection of Proteins in Organelles of Living Cells

AbstractRealizing protein analysis in organelles of living cells is of great significance for developing diagnostic and therapeutic methods of diseases. Fluorescent‐labeled antibodies with well imaging performance and high affinity are classical biochemical tools for protein analysis, while due to the inability to effectively enter into cells, not to mention organelles and the uncontrollable reaction sites that might cause antibodies inactivation when chemically modification, they are hard to apply to living cells. Inspired by the structure of fluorescent‐labeled antibodies, we designed as a universal detection platform that was based on the peptide‐conjugated probes (PCPs) and consisted of three parts: a) a rotor type fluorescent molecular scaffold for conjugation and signal output; b) the cell penetration protein recognition unit; c) the subcellular organelle targeting unit. In living cells, PCPs could firstly localize at organelles and then proceed protein specific recognition, thus jointly leading to the restriction of twisted intramolecular charge transfer and activation of fluorescence signal. As a proof‐of‐concept, six different proteins in three typical intracellular organelles could be detected by our platform through simply replacing the recognition sequence of proteins and matching organelle targeting units. The position and intensity of fluorescence signals demonstrated specificity of PCPs and universality of the platform.

Memory-specific encoding activities of the ventral tegmental area dopamine and GABA neurons.

Although the midbrain dopamine (DA) system plays a crucial role in higher cognitive functions, including updating and maintaining short-term memory, the encoding properties of the somatic spiking activity of ventral tegmental area (VTA) DA neurons for short-term memory computations have not yet been identified. Here, we probed and analyzed the activity of optogenetically identified DA and GABA neurons while mice engaged in short-term memory-dependent behavior in a T-maze task. Single-neuron analysis revealed that significant subpopulations of DA and GABA neurons responded differently between left and right trials in the memory delay. With a series of control behavioral tasks and regression analysis tools, we show that firing rate differences are linked to short-term memory-dependent decisions and cannot be explained by reward-related processes, motivated behavior, or motor-related activities. This evidence provides novel insights into the mnemonic encoding activities of midbrain DA and GABA neurons.

Monitoring the psychological component of higher education quality using ICT tools

This study examines the use of information and communication technologies (ICT) to monitor the psychological component of higher education quality. A literature review covers the concepts of higher education quality and its monitoring, the psychological factors involved, and the potential of ICT tools, including Google services. The study details a monitoring procedure using Google Forms to assess indicators like relationships between participants, satisfaction levels, and organisational climate. The computer program “Personnel – Ψ” is also presented as a tool for comprehensive psychological diagnostics of teachers. The advantages of using ICT tools for efficient data collection, analysis and management are discussed. The results demonstrate the feasibility and effectiveness of using ICT to improve monitoring of the psychological aspects of educational quality and make evidence-based decisions to enhance the educational environment. The use of user-friendly tools like Google Forms facilitates regular assessment of quality indicators and aids institutions in ensuring participant satisfaction and optimal conditions for learning and growth.

Assessment of Determinant Sources of Plastics and Microplastics Pollution in Inland Waterways Navigation in Lower River Niger

This study was aimed at assessing the impact of plastics and micro plastics pollution in Nigerian inland waterways operations. The main aim of this study is to provide a baseline assessment of plastic pollution in the River Niger, along with recommendations for further research. Using a survey and appropriate data analysis tools, the study investigated the determinant sources of plastic pollution in the River Niger, as well as the most significant impact and preferred means of plastic pollution and regulation. The main findings indicate that disposal of plastics is the determinant source of plastics and micro plastics in the River Niger. The most significant impact of plastic pollution was identified as reduction in the accessibility of certain areas of the river. Behavioral instrument is indicated to be the most preferred means of plastic pollution control and regulation. Hence, there is an urgent need for improved awareness among local communities. This can be achieved through education programs, which will help to reduce the use of single-use plastics while also reducing littering in these areas. The paper also recommends that more research be conducted on how these pollutants affect human health and ecosystems.

Behavioral intentions in embracing cloud-based accounting information systems

In facing the digital era and a more competitive environment, every organization is required to increasingly implement information systems that can facilitate various organizational needs. Cloud Based Accounting Information Systems (CBAIS), as part of information systems (IS), have also gained attention of organizations. This research was conducted to obtain empirical evidence of the interest in using CBAIS in various medium-sized companies in Indonesia measured using Institutional Theory. The data analysis tool used was SEM- PLS. The respondents in this study were accounting and finance departments employees working for logistics companies that still use conventional accounting information systems (non-cloud-based). A total of 89 responses were collected from 5 logistics companies in Indonesia. The research results indicated that mimetic pressure positively influenced the interest in using CBAIS, coercive pressure does not positively influence the interest in using CBAIS, and normative pressure positively influenced the interest in using CBAIS. An interesting aspect of this study is the absence of regulation or even demand in Indonesia that obligates the use of CBAIS. Hence, the use of CBAIS has not become a commonly adopted best practice in logistics companies.

Emerging Data Processing Methods for Single‐Entity Electrochemistry

AbstractSingle‐entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single‐entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five‐year advances in signal processing techniques for single‐entity electrochemistry and highlight their importance in obtaining high‐quality data and extracting effective features from electrochemical signals, which are generally applicable in single‐entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single‐molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single‐entity electrochemical measurements would revolutionize the field of single‐entity analysis, leading to new fundamental discoveries.

Emerging Data Processing Methods for Single-Entity Electrochemistry.

Single-entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single-entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five-year advances in signal processing techniques for single-entity electrochemistry and highlight their importance in obtaining high-quality data and extracting effective features from electrochemical signals, which are generally applicable in single-entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single-molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single-entity electrochemical measurements would revolutionize the field of single-entity analysis, leading to new fundamental discoveries.

Use of a risk assessment tool to determine the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is contentious. Most studies have focused on a zoonotic origin, but definitive evidence such as an intermediary animal host is lacking. We used an established risk analysis tool for differentiating natural and unnatural epidemics, the modified Grunow-Finke assessment tool (mGFT) to study the origin of SARS-COV-2. The mGFT scores 11 criteria to provide a likelihood of natural or unnatural origin. Using published literature and publicly available sources of information, we applied the mGFT to the origin of SARS-CoV-2. The mGFT scored 41/60 points (68%), with high inter-rater reliability (100%), indicating a greater likelihood of an unnatural than natural origin of SARS-CoV-2. This risk assessment cannot prove the origin of SARS-CoV-2 but shows that the possibility of a laboratory origin cannot be easily dismissed.

From manual to machine: assessing the efficacy of large language models in content analysis

ABSTRACT This study compares the performance of Large Language Models (LLMs) and human coders in predicting relational uncertainty from textual data. Employing various LLMs (gpt-4.0-turbo, gpt-3.5-turbo, Claude 2, llama7b-v2-chat, and llama13b-v2-chat), we found that these models perform comparably to human coders, with only minor differences in Mean Squared Error (MSE) values. However, not all LLMs performed equally, underscoring the importance of model selection. Our findings highlight the potential of LLMs as a scalable tool for content analysis, but also emphasize their nuanced application based on the specific research context. The study advances the discourse on the use of LLMs in content analysis and provides insights for future research in this rapidly evolving field.