Variety Of Formats Research Articles

Ontology Development for Asset Concealment Investigation: A Methodological Approach and Case Study in Asset Recovery

The concealment of assets is a critical challenge in financial fraud and asset recovery investigations, posing significant obstacles for creditors and regulatory authorities. National governments commonly possess the necessary data for detecting and combating this type of fraud, typically related to personal data and asset ownership. However, this information is often dispersed across different departments within the same government and sometimes in databases shared by other countries. This leads to difficulty semantically integrating this large amount of data in various formats and correlating entities through identifying hidden relationships, which are essential in this type of analysis. In this regard, this work proposes an ontology to support the data integration process in the domain of asset concealment and recovery and fill the gap in the existence of a public ontology for this domain. The applicability of this ontology in the context of integration between data from different departments and countries was validated. The use of the ontology in a pilot project in the context of a tool for investigating this type of fraud was conducted with a Brazilian government agency, and the users validated its applicability. Finally, a new method for constructing ontologies is proposed. The proposed process was evaluated during the asset concealment ontology building and proved to be more suitable than the similar processes analyzed concerning the partial reuse of existing ontologies and the construction of ontologies for data with a transnational scope.

A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification

In breast cancer research, diverse data types and formats, such as radiological images, clinical records, histological data, and expression analysis, are employed. Given the intricate nature of natural phenomena, relying on the features of a single modality is seldom sufficient for comprehensive analysis. Therefore, it is possible to guarantee medical relevance and achieve improved clinical outcomes by combining several modalities. The presen study carefully maps and reviews 47 primary articles from six well-known digital libraries that were published between 2018 and 2023 for breast cancer classification based on multimodal deep learning fusion (MDLF) techniques. This systematic literature review encompasses various aspects, including the medical modalities combined, the datasets utilized in these studies, the techniques, models, and architectures used in MDLF and it also discusses the advantages and limitations of each approach. The analysis of selected papers has revealed a compelling trend: the emergence of new modalities and combinations that were previously unexplored in the context of breast cancer classification. This exploration has not only expanded the scope of predictive models but also introduced fresh perspectives for addressing diverse targets, ranging from screening to diagnosis and prognosis. The practical advantages of MDLF are evident in its ability to enhance the predictive capabilities of machine learning models, resulting in improved accuracy across diverse applications. The prevalence of deep learning models underscores their success in autonomously discerning complex patterns, offering a substantial departure from traditional machine learning approaches. Furthermore, the paper explores the challenges and future directions in this field, including the need for larger datasets, the use of ensemble learning methods, and the interpretation of multimodal models.

Mastering meta-analysis in Microsoft Excel with MetaXL add-in: A comprehensive tutorial and guide to meta-analysis.

Meta-analysis, a powerful technique for combining effect estimates from multiple studies, enhances statistical power and precision. However, its adoption can be hindered by challenges in statistical interpretation and the complexity of specialized software. MetaXL, a freely available Microsoft Excel add-in, aims to mitigate these barriers by providing comprehensive support and facilitating seamless integration of meta-analytical results into research publications. This tutorial illustrates the practical application of MetaXL for synthesizing meta-analytical evidence, with a focus on common effect sizes and their presentation. This paper reintroduce MetaXL's functions and provide concise explanations of common effect sizes employed in meta-analysis. The tutorial delves into fundamental concepts such as the selection of appropriate effect sizes for pooling and the choice of meta-analytical models. Eight illustrative examples are presented, incorporating diverse effect sizes and data formats, including scenarios involving incidence rate ratios, weighted and standardized mean differences, hazard ratios, and prevalence. Additionally, key concepts in network meta-analysis are discussed, along with their implementation in MetaXL. MetaXL provides convenient access to data formatting templates tailored to various data types and effect sizes encountered in included studies. This tutorial offers researchers, particularly those with limited resources, detailed explanations and insights into commonly used methodologies for pooling effect sizes. Furthermore, it introduces the new Excel functions that comes with the MetaXL add-in. Accurate population of this function and adherence to the correct format are essential to ensure error-free analyzes.

Intelligent fatigue damage tracking and prognostics of composite structures utilizing raw images via interpretable deep learning

In recent years, prognostics gained attention in various industries by optimizing maintenance, boosting operational efficiency, and preventing costly downtime. Central to prognostics is the Remaining Useful Life (RUL), representing the critical time before system failure. Deep learning advancements facilitate RUL forecasting by extracting features from diverse data formats such as time series, images, or sequences thereof, in one, two, or three dimensions, respectively. Yet, predicting RUL from image sequences often relies heavily on resource-intensive techniques like digital image correlation, complicating data acquisition. To address challenges with high-dimensional data and unreliable models, this study introduces ISTRUST, an innovative Transformer-based architecture. ISTRUST (Interpretable Spatiotemporal TRansformer for Understanding STructures) tackles the dual challenges posed by high-dimensional data and the black-box nature of existing models. Leveraging Transformers’ attention mechanism, ISTRUST breaks down the spatiotemporal domain, effectively realizing interpretable RUL predictions under uncertainty using only sparse raw image sequences as input. Evaluated on fatigue-loaded composite samples showcasing crack propagation, ISTRUST interprets the relation between cracks and RUL via the attention mechanism. The results substantiate its capacity to interpret and clarify instances in which predictions may exhibit variability in accuracy. Through the attention mechanism, a strong correlation between the model’s spatiotemporal focus and the RUL predictions is established, making it, to the best of our knowledge, the first model to provide interpretable stochastic RUL predictions directly from sequential images of this nature.

A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management.

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

LAPOD, Latin-American paleomagnetic online database: An online interface to access paleomagnetic data from Latin America

We present the Latin-American Paleomagnetic Online Database (LAPOD), which includes an online tool for consulting and visualizing paleomagnetic data. This platform allows users to search, filter, and query over 2000 paleomagnetic data entries from locations across Latin America, including North America (Mexico and the Southwestern USA), Central America, and South America. Paleomagnetic data has been pre-validated for geographic locations and ages, enhancing its reliability for users.The platform features a robust database engine with up to 26 filters, allowing users to perform queries with high precision and avoiding data duplication, and to download data in various formats for personal use. It also offers graphical visualization of some data directly on screen. Color indicators are used to indicate data quality and magnetic polarity. All data included in LAPOD are properly referenced to their original authors, with links to the original published articles.LAPOD is proposed as a community-driven platform, allowing users to contribute their data and provide comments on their own and others' data. This fosters discussion about the reliability of paleomagnetic data and finally enhances our knowledge of the Earth's magnetic field in the region. This collaborative approach supports the development of new regional models of paleosecular variation with improved reliability. It may be repeated for other regions in the world, and ultimately lead to improved global field models. To access the platform, please visit: https://paleomagnetismo.org/pmagdb/

Spatial patterns of human–long-tailed macaque (Macaca fascicularis) conflicts in Java Island: A comparison of two secondary data sources

Human–macaque conflicts present a significant obstacle to conservation and management efforts in Indonesia. Long-tailed macaques, a species increasingly in conflict with humans, are often regarded as pests due to behaviors such as occasional raiding on agricultural land. This study aims to offer comprehensive insights into these conflicts by comparing data from two different sources and analyzing their spatial patterns to identify regions with significant conflict occurrences. Official records show a larger quantity of data, while online news supplements this. However, official records data quality varies, with diverse data formats and collection methods, whereas online news data consistently provide accessible information over a longer period. Both sources exhibit similar global spatial statistical patterns, indicating significant clustering. Local autocorrelation analysis identifies hotspot areas primarily at the borders of Yogyakarta, Central Java, and East Java. Discrepancies between the sources are evident in the High-Low class and the lack of clustering patterns in the western part of Java Island in official records. Despite similar spatial patterns, official records do not fully reflect the actual human–macaque conflict. Standardized methods and interview surveys are crucial for accurately assessing this conflict. Citizen science offers the potential for long-term wildlife monitoring, requiring systematic collaboration among stakeholders to mobilize experienced volunteers.

An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

AbstractDetailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.

Unified data integration and record identification framework for diverse data sources in healthcare demographics

Challenges of bridging data from different sources with diverse data formats are faced by organizations in the modern data management environment. Problems with disparate data sources leading to different formats and inconsistencies mean it can be challenging to get the right matching of data records, especially when information errors such as typos are present. The current lack of a standard pattern for data integration and record identification presents a major problem in ensuring the accurate identification of individual records across disparate sources. The variations in data formats and the abundance of errors, such as typographical mistakes in names, dates of birth, and gender, add to the complexity of this problem. Organizations face the challenge of ensuring the correctness and consistency of data across multiple datasets without a formalized methodology.

Criminal Investigation with the Help of Face Recognition

The Criminal Investigation and Face Recognition System (CIFRS) streamlines investigations with an intuitive interface and automated face recognition. Investigators effortlessly manage cases, set parameters, and upload facial images for analysis against a database. Real-time notifications alert investigators to potential matches, contributing to investigative efficiency. CIFRS supports diverse law enforcement management systems, allowing the download of case reports and data in various formats. The system also offers practice scenarios to familiarize users with face recognition technology. Keywords – Criminal Investigation, Face Recognition, Database Management, Law Enforcement.

Phase to intensity modulation and fading compensation using a single ring resonator for radio-over-fiber links.

In this study, we present a simulation-based analysis of radio-over-fiber (ROF) transmission links incorporating both phase modulation (PM) and a single ring resonator (RR) as the modulation transformer (MT). This configuration offers cost-effectiveness, enhanced operational stability, facile reconfiguration, and heightened robustness. The optimization of the RR involves a comprehensive adjustment of the power coupler coupling coefficient (k) and the roundtrip optical phase shift (φ) to attain superior characteristics in terms of power output, bandwidth, dispersion, and nonlinearity, individually. The simulation encompasses the transmission of diverse data formats, including QPSK, 16QAM, and 16QAM-based OFDM, modulated by the PM-RR system. The results reveal a 0.25 dB improvement in nonlinearity tolerance, increased power, and superior fading mitigation compared to the conventional intensity modulation (IM) approach. Furthermore, through careful tuning of the phase response, the Q factor of the PM-RR system exhibits an enhancement exceeding 40% over a 100 km fiber length when compared to the Mach-Zehnder modulator (MZM) system.

Aggregating intrinsic information to enhance BCI performance through federated learning

Insufficient data is a long-standing challenge for Brain–Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed framework can boost classification performance up to 8.4% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

Storage Standards and Solutions, Data Storage, Sharing, and Structuring in Digital Health: A Brazilian Case Study

The COVID-19 pandemic has highlighted the necessity for agile health services that enable reliable and secure information exchange, but achieving proper, private, and secure sharing of EMRs remains a challenge due to diverse data formats and fragmented records across multiple data silos, resulting in hindered coordination between healthcare teams, potential medical errors, and delays in patient care. While centralized EMR systems pose privacy risks and data format diversity complicates interoperability, blockchain technology offers a promising solution by providing decentralized storage, ensuring data integrity, enhancing access control, eliminating intermediaries, and increasing efficiency in healthcare. By focusing on a Brazilian case study, this paper explores the significance of EMR standards, security challenges, and blockchain-based approaches to promote interoperability and secure data sharing in the healthcare industry.

Distributed Privacy-Preserving Data Analysis in NFDI4Health With the Personal Health Train

Data sharing is often met with resistance in medicine and healthcare, due to the sensitive nature and heterogeneous characteristics of health data. The lack of standardization and semantics further exacerbate the problems of data fragments and data silos, which makes data analytics challenging. NFDI4Health aims to develop a data infrastructure for personalized medicine and health research and to make data generated in clinical trials, epidemiological, and public health studies FAIR (Findable, Accessible, Interoperable, and Reusable). Since this research data infrastructure is distributed over various partners contributing to their data, the Personal Health Train (PHT) complements this infrastructure by providing a required analytics infrastructure considering the distribution of data collections. Our research have demonstrated the capability of conducting data analysis on sensitive data in various formats distributed across multiple institutions and shown great potential to facilitate medical and health research.

GIS-based Spatial Analysis of the Evolution of Residential Developments; A Case-study of Sulaimani city, Iraq

This research is aimed at visualizing the historical evolution of residential landuse development of Sulaimani city based on GIS spatial analysis with the input of all of the relevant available data in various formats in order to determine the form of the city according to morphological classifications discussed in the literature of urban planning. Essentially, following the collection of data, processing, review of relevant literature and applied methods, this study draws a timeline for the spatial evolution of the city of Sulaimani in addition to constricting a spatial grid for morphological comparison of different growth stages and analysis on multiple aspects. Moreover, a number of criteria were established to evaluate spatial patterns based on published theories in the field of landuse urban planning. These indicators were formulated in a questionnaire survey and determined the main aspects of the city through visualizing the process of historical evolution up to modern date. Finally, the study presents transformations and addresses current issues, as well as recommends a number of possible solutions for residential landuse development in the city of Sulaimani.

Leveraging open data for reference services delivery in academic libraries

PurposeLibrarians play a critical role in curating, organizing and facilitating access to open data (OD) resources, supporting research, learning and information dissemination. This study aims to explore the use of OD for reference services delivery in academic libraries. It is believed that the concept of OD in librarianship refers to the practice of making publicly available data freely accessible, usable and shareable by anyone.Design/methodology/approachThis research examined how the utilization of OD may affect library services and operations. This paper used the systematic review of literature to answer the research questions; hence, it was a desk study.FindingsGiven that there has been recently a research shift in linking OD to library metadata, this study revealed some interesting findings. More specifically, it discovered that incorporating OD into reference services can provide several benefits and enhance the quality and relevance of the information provided to library users. In addition, this paper reported that OD enriches reference services by providing comprehensive, current and interdisciplinary information, supporting evidence-based research, enabling data visualization and analysis, fostering community engagement and promoting innovation.Originality/valueThis paper proposes that libraries that possess extensive collections are in a favorable position to embrace the shift toward becoming OD and open big data libraries. It is also believed that academic libraries, through reference services, meet the challenge of providing access to their institutional repositories by holding many sets of data in various formats and providing various interfaces, developing metadata systems for various data streams, supporting all ecosystems of software and data products for reformatting and reusing large and complex data, and support for data sets identifiers. By actively engaging with OD, librarians can play a vital role in facilitating access to information and promoting the use of open knowledge for academic pursuits.

The decomposition of the deep learning machine for specialized datasets for time minimizing of spatial information processing

For effective military management, the creation of complexes of automation tools, spatial information processing systems, primarily consolidated, is a priority task in the conditions of constant growth of data and requirements for their collection, transmission, storage, processing and use.
 The problem of consolidated processing of spatial information is related to the diversity of sources, the diversity of data formats, the diversity of acquisition and use, the diversity of processing etc. All that imply an extremely complex organizational and technical structure, a kind of ‘system of systems’.
 Deep Learning Machine (DLM) ensure the high accuracy of prediction. But such DLM should be matched for the military conditions of the usage where time restrictions and space lack are present.
 So, the effort to create a dataset for the maximum accuracy of the Deep Learning Machine rests on computing resources (in common research it is possible to overcome but in military sphere it isn’t). In military applied tasks, the criterion of time and overcoming uncertainties due to confrontation are critical. This allows us to put forward the hypothesis that it is impossible to achieve absolute accuracy in deep learning machine. Therefore, for variable tactical situations, it is advisable to create specialized datasets and achieve maximum efficiency from each iteration step (or their combination) using the decomposition method of the consolidated spatial information processing system. It is analysed the methods of scientific and technical solutions in the deep learning machine and the method of systematizing data types in existing tactical situations. In the end the detection and recognition system with a deep learning machine and a set of specialized datasets is proposed in the paper.
 The volume of each specialized dataset at the level of 103 enables ultra-high speed of information processing processes and allows a person to set up such a system of consolidated processing of spatial information without excessive organizational and time demands. The dataset itself is revealed.
 This principle of forming a dataset, or their sets, allows obtaining high-accurate and high -fast detection and recognition systems.

Abstract 6586: Updates to UALCAN, a comprehensive cancer proteogenomic data analysis platform for discovery research

Abstract Background: Recent advances in high throughput technologies and initiation of multiple consortium driven projects have led to generation of high volume of molecular data related to thousands of cancer patients. These high throughput data are extremely useful for novel biomarker identification, therapeutic target identification in tumor subclasses and allows numerous hypothesis driven cancer research projects. However, the sheer volume of data and diverse data formats presents tremendous challenge for researchers with no programming experience to access and analyze comprehensively. These factors prompted us to develop UALCAN (University of ALabama CANcer portal, Yes! You All Can) for comprehensive cancer data analysis in a user-friendly manner. UALCAN enables cancer researchers/clinicians in tumor subgroup based gene expression, protein expression and survival analyses. UALCAN is extremely popular among cancer researchers round the world with well over a “MILLION” site visits and cited over 3400 times in research articles. UALCAN integrates The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), as well as gene expression data from Children Brain Tumor Tissue Consortium (CBTTC). Users can also analyze non-coding RNA (miRNA and lncRNA) expression data from The Cancer Genome Atlas (TCGA) and the epigenetic data though the promoter DNA methylation analysis form the TCGA data. Results: UALCAN now enables user to study expression profile of ~10000 proteins in 14 CPTAC cancers and expression pattern and survival impact of 20,500 protein-coding, ~17,000 lncRNA and 1800 miRNA in 33 TCGA cancers. It also facilitates analysis of gene expression pattern in pediatric brain cancer. User-friendly graphical user interface of UALCAN facilitates a) analyzing protein/non-coding RNA expression in tumor subgroups based on race, stage, age and molecular types; b) identifying top differentially expressed lncRNA/miRNA in specific cancer subtype; c) listing potential gene targets of non-coding RNA; d) performing pan-cancer analysis and e) determining impact of gene expression on patient’s overall survival. Conclusions: UALCAN is freely available at http://ualcan.path.uab.edu (Google: UALCAN) This dynamic data portal provides constant updates that empower cancer researchers in studying gene and protein level expression and identifying tumor subgroup specific non-coding RNA biomarkers. UALCAN serves a one-stop platform for access, study, analyze and leverage large cancer datasets. Citation Format: Darshan Shimoga Chandrashekar, Sooryanarayana Varambally, Santhosh Kumar Karthikeyan, Praveen Kumar Korla, Henalben Patel, Mohammad Athar, Upender Manne, George J. Netto, Ahmedur Rahman Shovon, Sidharth Kumar, Zhaohui S. Qin, Chad J. Crieghton. Updates to UALCAN, a comprehensive cancer proteogenomic data analysis platform for discovery research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6586.

A Docker-based Operation and Maintenance Method for New-Generation Command and Control Systems

New-generation command and control systems face a number of problems in operation and maintenance, including different programming languages used for subsystems, complex environment configuration, and diverse data formats, leading to slow system deployment, various types of managed data, and difficult operation and maintenance. This paper proposes the use of the Docker containers to wrap the system, deploy the networked control system based on the encapsulated service processes and relevant environment configuration and dependencies, and build a Docker-based operation and maintenance model for command and control systems. As for the communication between containers, the mature RocketMQ architecture is adopted for interactions based on message buses. Container migration and restart are used to improve the deployment, operation and maintenance capabilities of the entire system. The design is realized and simulated on distributed machines. This paper explains relevant concepts, design ideas, and implementation pathways.

Quality Management System for an IoT Meteorological Sensor Network—Application to Smart Seoul Data of Things (S-DoT)

Meteorological data with a high horizontal resolution are essential for user-specific weather application services, such as flash floods, heat waves, strong winds, and road ice, in urban areas. National meteorological observation networks, such as the Automated Synoptic Observing System (ASOS) and Automated Weather System (AWS), provide accurate but low horizontal resolution data to address urban-scale weather phenomena. Many megacities are constructing their own Internet of Things (IoT) sensor networks to overcome this limitation. This study investigated the status of the smart Seoul data of things (S-DoT) network and the spatial distribution of temperature on heatwave and coldwave event days. The temperature at above 90% of S-DoT stations was higher than that at the ASOS station, mainly because of different surface covers and surrounding local climate zones. A quality management system for an S-DoT meteorological sensor network (QMS-SDM) comprising pre-processing, basic quality control, extended quality control, and data reconstruction using spatial gap-filling was developed. The upper threshold temperatures for the climate range test were set higher than those adopted by the ASOS. A 10-digit flag for each data point was defined to discriminate between normal, doubtful, and erroneous data. Missing data at a single station were imputed using the Stineman method, and the data with spatial outliers were filled with values at three stations within 2 km. Using QMS-SDM, irregular and diverse data formats were changed to regular and unit-format data. QMS-SDM application increased the amount of available data by 20-30%, and significantly improved data availability for urban meteorological information services.