Large Volumes Of Data Research Articles

Requirements and their implementation for an intelligent decision-making system for the identification and classification of objects of cultural value

The article considers the problem of development and analysis of requirements and ways of their implementation for an intelligent decision support system for identification and classification of objects of cultural value. The work analyzes both general standards of software development and specialized standards relating to intelligent systems, modern methodologies and practical instructions are also taken into account. The work defines and analyzes such system requirements as: speed of processing large volumes of data, accuracy of identification, integration with other information systems and databases, data protection and information security, scaling, adaptation to new types of data and object behavior models, interface user, processing and analysis of various types of data, cross-platform and forecasting based on available data to support decision-making. The article also defines the complexity and complexity of the approach to solving the given task, the prospects and ways of further research of this subject area are determined.

Electric load forecasting under false data injection attacks via denoising deep learning and generative adversarial networks

AbstractAccurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks.

Social media usage and sustainable performance in manufacturing supply chains: exploring dynamic capabilities

Due to the global impact of COVID-19, businesses and supply chains worldwide have been disrupted, leading to challenges for manufacturing firms and customers. Social media usage (SMU) is a vital and valuable tool that builds customer-centric supply chains by effectively communicating large volumes of valuable data. This study examines the influence of social media usage on the development of dynamic capabilities, including supply chain sensing capabilities (SCSC), knowledge sharing capability (KSC), and social capital (SC), which are crucial for constructing agile and resilient supply chains that can effectively respond to dynamic and turbulent environments, ultimately enhancing sustainable business performance. To assess the proposed investigation model, data was gathered through a structured questionnaire administered to supply chain members currently engaged with manufacturing firms. The evaluation of the proposed hypotheses is carried out through the application of structural equation modeling (SEM). The results of this study offer empirical evidence supporting the proposition that SMU within manufacturing firms promotes the development of sensing capabilities, knowledge-sharing capability, and social capital. Furthermore, this study tests the moderation role of environmental dynamism in the relationship between social media use and sustainable business performance. This underscores the pivotal role played by SMU in facilitating the adaptation and responsiveness of the supply chain to dynamic and uncertain environments, thereby contributing to the long-term sustainability and competitiveness of organizations.

Named entity recognition and its role in unstructured data analysis

In today's digital world, where vast amounts of unstructured data are generated every day, the ability to efficiently process this information is key for many industries. Unstructured data, which includes text files, emails, video, audio, images, and other forms of media, is the bulk of digital data and requires specialized tools to analyze it. Natural Language Processing (NLP) and Named Entity Recognition (NER) are two key technologies that enable the transformation of unstructured data into structured information that can be used for a variety of applications. Natural Language Processing enables machines to understand, interpret, manipulate and generate human language, opening up possibilities for deep analysis of textual data. This includes identifying key words, phrases, themes, and emotional nuances in texts. NER, as an important component of Natural Language Processing, specializes in identifying and classifying named entities in the text into certain categories, such as names of persons, organizations, locations, dates, times, and others. This allows you to automate the processes of sorting, categorizing and analyzing information. However, working with Natural Language Processing and Named Entity Recognition faces a number of challenges. The large volume and variety of data make it difficult to collect, store and analyze it. Lack of standardization can lead to problems with interoperability and integration of different data sources. In addition, there are challenges related to the recognition of named entities, in particular, distinguishing between the same names belonging to different persons and understanding the context in which the names are used. Despite these challenges, the outlook for Natural Language Processing and Named Entity Recognition looks bright, with continued innovations in artificial intelligence and machine learning promising to improve the accuracy and efficiency of these technologies in the future.

Prospects of Using Information Technologies in the Transformation of National Financial Reporting

This article covers the prospects of applying information technologies in the transformation of national financial reporting, their role and significance, achieving time and labor efficiency in the preparation of financial reports using software products, preventing errors when working with large volumes of data, and completing tasks faster and with higher quality using programs based on information technologies rather than traditional methods. Additionally, the article addresses issues such as creating opportunities for global access to data through the implementation of databases and cloud technologies. Additionally, the article mentions that the creation, presentation, interpretation, and transmission of a company's financial reports using digital technologies become much more efficient and faster. It is also mentioned that the integrity and reliability of a company’s financial reports are enhanced through software created with digital technologies. Additionally, these technologies allow for faster report generation and provide users with wide-ranging opportunities to access the reports from anywhere and make decisions. Additionally, the article discusses issues such as transforming the company’s accounting system based on international financial reporting standards and thereby entering the global market with accurate and reliable information. It is specifically emphasized that the application of international financial reporting standards in the preparation of financial reports, studying their importance and necessity, and researching their theoretical and methodological aspects, provide a priority in attracting users of these financial reports, mainly investors. Additionally, the article highlights that the presentation of transparent and fully disclosed information sets serves as a reliable source.

Assessing Darkness of the Human Kinome from a Medicinal Chemistry Perspective.

In drug discovery, human protein kinases (PKs) represent one of the major target classes due to their central role in cellular signaling, implication in various diseases as a consequence of deregulated signaling, and notable druggability. Individual PKs and their disease biology have been explored to different degrees, giving rise to heterogeneous functional knowledge and disease associations across the human kinome. The U.S. National Institutes of Health previously designated 162 understudied ("dark") human PKs and lipid kinases due to the lack of functional annotations and high-quality molecular probes for functional investigations. Given the large volumes of available PK inhibitors (PKIs) and activity data, we have systematically analyzed the distribution of PKIs and associated data at different confidence levels across the human kinome and distinguished between chemically explored, underexplored, and unexplored PKs. The analysis provides a medicinal chemistry-centric view of PK exploration and further extends prior assessment of the dark kinome.

Beyond neurons: computer vision methods for analysis of morphologically complex astrocytes

The study of the geometric organization of biological tissues has a rich history in the literature. However, the geometry and architecture of individual cells within tissues has traditionally relied upon manual or indirect measures of shape. Such rudimentary measures are largely a result of challenges associated with acquiring high resolution images of cells and cellular components, as well as a lack of computational approaches to analyze large volumes of high-resolution data. This is especially true with brain tissue, which is composed of a complex array of cells. Here we review computational tools that have been applied to unravel the cellular nanoarchitecture of astrocytes, a type of brain cell that is increasingly being shown to be essential for brain function. Astrocytes are among the most structurally complex and functionally diverse cells in the mammalian body and are essential partner cells of neurons. Light microscopy does not allow adequate resolution of astrocyte morphology, however, large-scale serial electron microscopy data, which provides nanometer resolution 3D models, is enabling the visualization of the fine, convoluted structure of astrocytes. Application of computer vision methods to the resulting nanoscale 3D models is helping reveal the geometry and organizing principles of astrocytes, but a complete understanding of astrocyte structure and its functional implications will require further adaptation of existing computational tools, as well as development of new approaches.

Occupancy Map Guided Attributes Artifacts Removal for Video-based Point Cloud Compression

Point clouds offer realistic three-dimensional (3-D) representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-based Point Cloud Compression (V-PCC) converts their texture into two-dimensional (2-D) attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This paper proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained.

Integrating radiological technology in environmental health surveillance to enhance public safety

The integration of radiological technology into environmental health surveillance represents a significant advancement in enhancing public safety. Radiological technologies, including advanced imaging and monitoring systems, offer powerful tools for detecting and assessing environmental hazards that impact public health. This review explores the potential benefits, methodologies, and challenges associated with integrating radiological technology into environmental health surveillance. Recent advancements in radiological technology, such as high-resolution imaging systems and portable radiation detectors, have greatly improved the ability to monitor and analyze environmental radiation levels with precision. These technologies enable real-time detection of radiation exposure, providing critical data for assessing risks associated with radiation sources, such as industrial activities, nuclear power plants, and natural radioactive materials. By integrating these tools into environmental health surveillance frameworks, public health agencies can enhance their capacity to identify and address radiation-related hazards more effectively. The application of radiological technology in environmental health surveillance facilitates comprehensive monitoring of radiation levels across various environments, including urban, rural, and industrial areas. This data is crucial for identifying contamination hotspots, tracking changes in radiation levels over time, and evaluating the effectiveness of radiation control measures. Additionally, the integration of radiological technology supports the development of predictive models for assessing potential health impacts and guiding emergency response strategies in the event of radiation incidents. Despite its advantages, integrating radiological technology into environmental health surveillance presents several challenges. These include the high costs of advanced equipment, the need for specialized training for personnel, and the management of large volumes of data generated by monitoring systems. Addressing these challenges requires a coordinated approach involving government agencies, research institutions, and industry stakeholders to ensure effective implementation and sustainability. In conclusion, integrating radiological technology into environmental health surveillance has the potential to significantly enhance public safety by providing accurate and timely data on radiation exposure. Continued innovation and collaboration in this field will be essential for optimizing the benefits of radiological technology and addressing the challenges associated with its integration into environmental health monitoring systems. Keywords: Integrating; Radiological Technology; Environmental Health Surveillance; Enhance; Public Safety

Cybersecurity Challenges in the Era of Chinese Electric Passenger Vehicles

The present research focuses on investigating the security of data transmitted by electric passenger vehicles originating from the People’s Republic of China, particularly when sent for remote processing. Initially, the study examines the factors contributing to the widespread presence of Chinese vehicles in the European market. Additionally, it explores the measures undertaken by the European Union to safeguard the information and data security of its member states. Following this investigation, an in-depth qualitative research method involving semi-structured interviews was conducted. Ten experts in information security were interviewed, and their insights were analysed concerning the identified challenges. The primary objective of this publication is to draw attention to the conceptual deficiencies that impede the protection of data and information, highlighting how a large volume of data collected from a third country, in this case, electric passenger vehicles, may pose national security risks.

Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases.

Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management.

Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery

Accurate and efficient estimation of biochemical traits, including leaf index area (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC), is crucial for crop growth monitoring in agricultural management. Recent advancements in unmanned aerial vehicle (UAV) multispectral remote sensing have enabled fast and cost-effective measurements of these traits. However, traditional statistical regression models trained on specific datasets lack scalability and transferability across practical field conditions without retraining. This study proposed an efficient physics-informed transfer learning model (PITL) for winter wheat biochemical traits estimation from UAV multispectral data. The PITL integrates the strengths of physical radiative transfer simulations and deep neural network architectures through transfer learning to improve the estimation of biochemical traits from UAV multispectral data. The PITL was tested with convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM) architectures. Results indicated that PITLDNN had better accuracy than PITLCNN and PITLLSTM models in predicting LAI (R2=0.94, RMSE = 0.32 m2/m2), LCC (R2=0.81, RMSE = 5.20 μg/cm2) and CCC (R2=0.928, RMSE = 0.2 g/m2). Moreover, PITLDNN demonstrated higher capability in computational efficiency, making it suitable for processing large volumes of UAV multispectral data in crop growth monitoring applications. Furthermore, PITL's integration of radiative transfer knowledge with labeled field data yielded higher predictive accuracy compared to physically-based inversion model, pure data-driven deep neural network approaches, and hybrid models. This study highlighted the performance of PITLDNN in accurately and efficiently quantifing biochemical traits from UAV multispectral data, thereby providing timely and accurate information for guiding crop growth monitoring applications.

Synthetic minority oversampling and iterative fluorescence-suppression integrated algorithm for Raman spectrum pesticide detection system

Raman spectrum preprocessing method for automatic denoising and suppression of the fluorescent background. In this method, noise is reduced using wavelet transform, and a modified polynomial curve fitting method is implemented such that an algorithm can independently identify the optimal curve parameters for fluorescent background suppression. To address the problem of imbalanced datasets, the present study employed a synthetic minority oversampling technique to increase the volume of data in minority classes. This technique enables the prediction of pesticides that are otherwise difficult to detect, and the prediction accuracy is comparable to that of detection with large data volumes. The proposed convolutional neural network model was verified to accurately identify the type of single pesticides and composition of mixed pesticides. The prediction accuracy for mixed pesticides reached 99.1%.

Machine learning misclassification networks reveal a citation advantage of interdisciplinary publications only in high-impact journals

Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classification can be informative about properties of the underlying categories. Here we classify a large volume of academic publications using only the abstract as information. From the publications that are classified differently by journal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in two topics at the core of the science of science: (1) Mapping out the interplay of disciplines. We show that this misclassification network is informative about the interplay of academic disciplines and it is similar to, but distinct from, a citation-based map of science, where nodes are scientific disciplines and an edge indicates a strong co-citation count between publications in these disciplines. (2) Analyzing the success of interdisciplinarity. By evaluating the citation patterns of publications, we show that misclassification can be linked to interdisciplinarity and, furthermore, that misclassified articles have different citation frequencies than correctly classified articles: In the highest 10 percent of journals in each discipline, these misclassified articles are on average cited more frequently, while in the rest of the journals they are cited less frequently.

STATISTICAL ANALYSIS OF THE FINANCIAL SITUATION OF THE BANKING SECTOR

Statistical analysis is the process of collecting large volumes of data and then using statistics and other data analysis techniques to identify trends, patterns, and insights. If you're a whiz at data and statistics, statistical analysis could be a great career match for you. The statistical analysis of the situation in the banking sector is a process of studying the main indicators that characterize banking activity in modern conditions. An important direction in the analysis of the banking sector is to determine its reliability and sustainability. The reliability and stability of the banking system are largely determined by the financial performance of the banks. With a dynamic, structural, factor analysis of analytical and statistical information about the processes taking place in this area, tendencies in the banking sector are revealed. The statistical analysis covers the main indicators of the bank's resource base and the efficiency of its operation. The statistical analysis analyzes the main trends of changes in the structure of liabilities and assets of the banks and provides an analysis of the coefficients of loans and deposits. The financial analysis of the state of the banking sector includes the study of the factors that influence the activities of banking institutions. Statistics in banking is becoming increasingly important to the extent that modern banking at all levels would be impossible without the application of statistical methods. In the professional world, banking statistical analysts take raw data and find correlations between variables to reveal patterns and trends to relevant stakeholders. Working in a wide range of different fields, banking statistical analysts are responsible for new scientific discoveries, improving the health of our communities, and guiding business decisions.

Canalis: A Throughput-Optimized Framework for Real-Time Stream Processing of Wireless Communication

Stream processing, which involves real-time computation of data as it is created or received, is vital for various applications, specifically wireless communication. The evolving protocols, the requirement for high-throughput, and the challenges of handling diverse processing patterns make it demanding. Traditional platforms grapple with meeting real-time throughput and latency requirements due to large data volume, sequential and indeterministic data arrival, and variable data rates, leading to inefficiencies in memory access and parallel processing. We present Canalis, a throughput-optimized framework designed to address these challenges, ensuring high-performance while achieving low energy consumption. Canalis is a hardware-software co-designed system. It includes a programmable spatial architecture, FluxSPU (Flux Stream Processing Unit), proposed by this work to enhance data throughput and energy efficiency. FluxSPU is accompanied by a software stack that eases the programming process. We evaluated Canalis with eight distinct benchmarks. When compared to CPU and GPU in mobile SoC to demonstrate the effectiveness of domain specialization, Canalis achieves an average speedup of 13.4× and 6.6×, and energy savings of 189.8× and 283.9×, respectively. In contrast to equivalent ASICs of the benchmarks, the average energy overhead of Canalis is within 2.4×, successfully maintaining generalizations without incurring significant overhead.

Improved Bayesian model updating of geomaterial parameters for slope reliability assessment considering spatial variability

In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the “curse of dimensionality”. Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment.

StarTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It’s worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.

User Sentiment Analysis Based on Securities Application Elements.

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.

An effective drift-diffusion model for pandemic propagation and uncertainty prediction

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.