Multidimensional Data Analysis Research Articles

Dynamics of phytoplankton communities in the Baltic Sea: insights from a multi-dimensional analysis of pigment and spectral data—part I, pigment dataset

This study aims to investigate the seasonal and spatial distribution of surface phytoplankton communities in the Baltic Sea, using pigment analysis and hydrological parameters. Data were collected during six oceanographic campaigns between 2005 and 2008, including high-performance liquid chromatography (HPLC) pigment characterization and hydrological measurements. The first part of this comprehensive study was focused on the HPLC phytoplankton pigment dataset in relation to hydrological conditions. The research highlighted the importance of high-quality input data for accurate taxonomic analysis. Several unsupervised machine learning approaches, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and network-based community detection analysis (NCA), were used to analyze the data and identify phytoplankton communities based on biomarker pigments. Five main phytoplankton communities were identified: diatoms, dinoflagellates, cryptophytes, green algae, and cyanobacteria. The results evidenced distinct seasonal patterns, with diatom blooms dominating in spring, cyanobacterial blooms in mid-summer, and haptophyte and dinoflagellate peaks occurring in late summer and autumn. While PCA and NCA provided consistent insights into community structure, HCA offered less clarity in distinguishing between groups. The results of the statistical analysis were then compared with those of traditional approaches such as CHEMTAX and region-specific bio-optical algorithms, providing new perspectives on the taxonomic composition of phytoplankton groups. This study provides valuable insights into phytoplankton dynamics in the Baltic Sea and the effectiveness of different analytical approaches in understanding community structure, providing metrics that can enhance current and future advancements in remote sensing, including support for hyperspectral ocean color remote sensors.

Sustainable Business Models: An Empirical Analysis of Environmental Sustainability in Leading Manufacturing Companies

This study thoroughly investigates the role of sustainable business models in enhancing environmental sustainability in leading manufacturing companies. Guided by the United Nations Sustainable Development Goals (SDGs), we empirically analyse the integration of sustainability goals into corporate strategies. This study identifies sustainable business models based on an analysis of the sustainability reports published on the website, examining the strategies and action plans declared by 30 companies that are leaders in the sustainability industry, according to their Dow Jones Sustainability Index World (DJSI World) and S&P Global ESG Scores. The strategies considered are aligned with the following specific sustainability development goals: 6 (water security); 7 (renewable energy); 12 (responsible consumption and production); and 13 (climate action). The dataset contains several variables, each reflecting a particular facet of a company’s environmental sustainability, as follows: energy consumption; greenhouse gas emissions; waste management strategies; and water conservation initiatives. We use a multidimensional data analysis technique called multiple correspondence analysis (MCA). After using MCA, we use a hierarchical clustering algorithm with the aim of classifying the different companies. Our findings underscore the presence of seven clusters of companies. Compared to the well-established literature on the topic of sustainable business, the innovative contribution of this study is linked to the identification of reaction time as a strategic variable explaining the different sustainable business models. The study makes it clear that the different business models are linked to reaction time to strategic alignment with environmental objectives. The country in which the company is based is also important. This study provides practical insights for companies aiming to align their practices with SDGs. In fact, the time variable provides important information in this regard and makes it possible to identify different approaches to sustainability as well as strong and weak sustainable business models; the former are characterised by a medium long-term strategic orientation towards environmental sustainability, which can be interpreted as the desire to undertake more solid and structured environmental sustainability strategies.

Single-Cell Multi-Dimensional data analysis reveals the role of ARL4C in driving rheumatoid arthritis progression and Macrophage polarization dynamics

Rheumatoid arthritis (RA) is an enduring autoimmune inflammatory condition distinguished by continual joint inflammation, hyperplasia of the synovium, erosion of bone, and deterioration of cartilage.Fibroblast-like synoviocytes (FLSs) exhibiting “tumor-like” traits are central to this mechanism.ADP-ribosylation factor-like 4c (ARL4C) functions as a Ras-like small GTP-binding protein, significantly impacting tumor migration, invasion, and proliferation.However, it remains uncertain if ARL4C participates in the stimulation of RA FLSs exhibiting “tumor-like” features, thereby fostering the advancement of RA. In our investigation, we unveiled, for the inaugural instance, via the amalgamated scrutiny of single-cell RNA sequencing (scRNA-seq) and Bulk RNA sequencing (Bulk-seq) datasets, that activated fibroblast-like synoviocytes (FLSs) showcase high expression of ARL4C, and the ARL4C protein expression in FLSs derived from RA patients significantly surpasses that observed in individuals with osteoarthritis (OA) and traumatic injury (trauma).Silencing of the ARL4C gene markedly impeded the proliferation of RA FLSs by hindered the transition of cells from the G0/G1 phase to the S phase, and intensified cell apoptosis and diminished the migratory and invasive capabilities. Co-culture of ARL4C gene-silenced RA FLSs with monocytes/macrophages significantly inhibited the polarization of monocytes/macrophages toward M1 and the repolarization of M2 to M1.Furthermore, intra-articular injection of shARL4C significantly alleviated synovial inflammation and cartilage erosion in collagen-induced arthritis (CIA) rats.In conclusion, our discoveries propose that ARL4C assumes a central role in the synovial inflammation, cartilage degradation, and bone erosion associated with RA by triggering the PI3K/AKT and MAPK signaling pathways within RA FLSs.ARL4C holds promise as a prospective target for the development of pharmaceutical agents targeting FLSs, with the aim of addressing RA.

Spatial, Spectral and Time Resolution: Tackling the Challenges of Multidimensional Luminescence Data Analysis with LumiSpy

Spatial, Spectral and Time Resolution: Tackling the Challenges of Multidimensional Luminescence Data Analysis with LumiSpy

Digital Twin System of Substation Equipment

This paper adopts the following technical solutions to achieve: a digital twin system for substation, including: database, database configuration module, data warehousing module, background data processing module and front-end browsing module; The front-end browsing module is used to display the data sent by the background data processing module in the corresponding form according to the user's selection; The form includes building the three-dimensional model of the substation based on BIM technology, and displaying the corresponding real-time data and status in the three-dimensional components of the three-dimensional model. Compared with the existing technology, it organically integrates the three-dimensional model and panoramic monitoring data of the substation, realizes the three-dimensional panoramic display of substation assets and the acquisition of substation monitoring data, improves the spatial experience of daily operation and maintenance comprehensive monitoring, and provides a solid data platform for the subsequent in-depth mining and intelligent analysis of multidimensional data of the substation.

Enhanced Estimation of Traffic Noise Levels Using Minute-Level Traffic Flow Data through Convolutional Neural Network

The advent of high-resolution minute-level traffic flow data from video surveillance on roads has opened up new opportunities for enhancing the estimation of traffic noise levels. In this study, we propose an innovative method that utilizes time series traffic flow data (TSTFD) to estimate traffic noise levels using a deep learning Convolutional Neural Network (CNN). Unlike traditional traffic flow data, TSTFD offer a unique structure and composition suitable for multidimensional data analysis. Our method was evaluated in a pilot study conducted in Foshan City, China, utilizing traffic flow information obtained from roadside video surveillance systems. Our results indicated that the CNN-based model surpassed traditional data-driven statistical models in estimating traffic noise levels, achieving a reduction in mean squared error (MSE) by 10.16%, mean absolute error (MAE) by 4.48%, and an improvement in the coefficient of determination (R²) by 1.73%. The model demonstrated robust generalization capabilities throughout the test period, exhibiting mean errors ranging from 0.790 to 1.007 dBA. However, the model’s applicability is constrained by the acoustic propagation environment, demonstrating effectiveness on roads with similar surroundings while showing limited applicability to those with different surroundings. Overall, this method is cost-effective and offers enhanced accuracy for the estimation of traffic noise level.

Error analysis of machine learning methods as the educational background for its use skills formation

Introduction. Machine learning methods and elements of artificial intelligence are used to analyze random data, processes and signals. The study of relevant tools is already included in the various levels curricula. The purpose of the study is to demonstrate, using examples available to students of various specialties, that the error analysis of machine learning methods in solving specific tasks can be the basis in the educational process for the skills formation of using artificial intelligence elements.Materials and Methods. For processing random signals and data, widely available software is used: Microsoft Excel for preparing training and test samples, the Deductor analytical platform for implementing machine learning algorithms. As an example, quasi-harmonic signals with random parameters are processed for technical specialties, and the results of psycho diagnostics are used to process multidimensional random data.Results. As a typical solution of approximation technical problems, direct propagation neural network errors in using to determine random signal parameters are analyzed. As a solution of classification problems, multidimensional random data with different dimensions were processed using neural networks and the "decision tree" method. The advantages of the combined use of these two machine learning methods are analyzed. These examples and their analysis were tested in classes with university students in the disciplines of "Digital Signal Processing" and "Fundamentals of Statistics".Discussion and Conclusions. The statistical features of the obtained results, the possibilities of reducing the training sample and selective analysis of multidimensional random data are discussed. It is shown that an adequate assessment of the machine learning methods errors can significantly expand the possibilities of their application, and can be the basis for the formation of skills for their use.

Constrained tensorial total variation problem based on an alternating conditional gradient algorithm

The present paper aims to regularize ill-posed high-dimensional problems using a constrained total variation minimization approach. The analysis of the continuous model via the dual formulation converts this minimization problem into a constrained minimax problem. Three main issues stand before the resolution of this problem: The minimization over a constraint, the high dimensional representation of the model, and the non-linearity and non-differentiability of the cost function. On the one hand, multidimensional data analysis will be performed using the tensor representation. On the other hand, a new alternating approach based on a proposed pseudo-Lagrangian operator will be developed to deal with the non-linearity and the non-differentiability of this constrained optimization problem. The proposed technique aims to decompose the main tensorial problem into feasible subproblems that can be solved using the conditional gradient method. The convergence of the proposed algorithm is proved and some numerical results are given to illustrate the effectiveness of the presented approach in different fields of image and video processing.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

Development of a Decision Support System for University Administration: Integrating Business Process Management and Multidimensional Data Analysis

Development of a Decision Support System for University Administration: Integrating Business Process Management and Multidimensional Data Analysis

Multi-omics analysis reveals the association between specific solute carrier proteins gene expression patterns and the immune suppressive microenvironment in glioma.

Glioma is the most prevalent malignant brain tumour. Currently, reshaping its tumour microenvironment has emerged as an appealing strategy to enhance therapeutic efficacy. As the largest group of transmembrane transport proteins, solute carrier proteins (SLCs) are responsible for the transmembrane transport of various metabolites and ions. They play a crucial role in regulating the metabolism and functions of malignant cells and immune cells within the tumour microenvironment, making them a promising target in cancer therapy. Through multidimensional data analysis and experimental validation, we investigated the genetic landscape of SLCs in glioma. We established a classification system comprising 7-SLCs to predict the prognosis of glioma patients and their potential responses to immunotherapy and chemotherapy. Our findings unveiled specific SLC expression patterns and their correlation with the immune-suppressive microenvironment and metabolic status. The 7-SLC classification system was validated in distinguishing subgroups within the microenvironment, specifically identifying subsets involving malignant cells and tumour-associated macrophages. Furthermore, the orphan protein SLC43A3, a core member of the 7-SLC classification system, was identified as a key facilitator of tumour cell proliferation and migration, suggesting its potential as a novel target for cancer therapy.

Разработка и реализация протокола поведенческого эксперимента для оценки когнитивных способностей и социального поведения мышей после стресса в раннем возрасте

<p style="text-align: justify;">This manuscript presents a protocol designed for the comprehensive investigation of early life stress (ELS) outcomes and a feasibility study conducted with this protocol. ELS alters normal development by interfering at various levels: hormonal changes, brain cellular architecture, epigenome, and chromosomal structural elements. The protocol combines classic behavioral tests with advanced molecular techniques to obtain comprehensive data and thus uncover the underlying mechanisms of ELS. In this protocol, the main source of stress is maternal separation. Briefly, a group of C57Bl/6 mice undergoes maternal separation; then, mice perform the radial maze test and the resident-intruder test. As a control, another group of mice stays undisturbed and performs the same behavioral tests in the same timeframe. After the behavioral tests, biosamples are collected, including urine for corticosterone measurements, peripheral blood, hippocampus, amygdala, and prefrontal cortex tissues for DNA isolation and its downstream analyses (DNA methylation profiling and telomere length measuring), and whole brains for immunohistochemistry analysis of the glucocorticoid receptor density. This protocol was successfully tested as a feasibility study for a large-scale investigation that addresses potential flaws to establish a robust methodology. This paper reports on a comprehensive approach to examining multiple aspects of development that interrogates a holistic analysis of multilayer and multidimensional data and may contribute valuable insights for both animal and human studies.</p>

Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques.

Multi-Dimensional Data Analysis Platform (MuDAP): A Cognitive Science Data Toolbox

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease.

GLC-Frame: A Framework and Library for Exploration of Multidimensional Data with General Line Coordinates

General Line Coordinates (GLC) are a relatively new set of line-based representations for visualizing multidimensional data with the distinctive characteristics of being reversible and lossless. Given these characteristics, the GLC have a high potential for exploratory multidimensional data analysis, however only partial implementations of some of the GLC techniques are available for the visualization community. In this paper, we present the GLC-Frame, an online exploration tool that supports a dual view and allows users to upload their own dataset and interactively explore the different GLC representations without writing code. We also present the GLC-Vis Library, an open-source data visualization library supporting GLC along with traditional interactions. Finally, we provide a set of usage examples showing how the different techniques behave in both the occlusion and the cluster identification problem. In addition, we present the interactions on GLC representations using the cars dataset. Both the GLC-Frame and the GLC-Vis Library provide an exploration space that will allow the visualization community to use these new techniques and evaluate their potential.

A novel impact indentation technique with dynamic calibration method for measurement of dynamic mechanical properties

High strain rates and continuous real-time data acquisition are difficult to balance in existing impact indentation techniques. Meanwhile, the inaccuracy of the testing P-h curves limits the development of quantitative applications for dynamic testing of mechanical properties at micro region. This article develops a momentum exchange impact indentation testing method, which extends the testing range of strain rate to above 105 s−1, and at the same time avoids secondary impact. Impact indentation tests are conducted on single-crystal aluminum specimens with impact velocity from 76.9 to 176 mm/s. Based on calibrated data, the comparison of calculation methods for dynamic hardness is carried out. The dynamic elastic modulus is obtained by means of the Oliver-Pharr method, which increases from 71.9 to 89.4 GPa with increasing impact velocity. A multi-dimensional data analysis method is proposed based on the obtained experimental data to quantitatively study the damping and stiffness characteristics of materials. This testing method is particularly useful for quantitatively obtaining the micro region dynamic mechanical properties of solid materials at high strain rates.

Multidimensional data analysis revealed thyroiditis-associated TCF19 SNP rs2073724 as a highly ranked protective variant in thyroid cancer.

Thyroid cancer represents the most prevalent malignant endocrine tumour, with rising incidence worldwide and high mortality rates among patients exhibiting dedifferentiation and metastasis. Effective biomarkers and therapeutic interventions are warranted in aggressive thyroid malignancies. The transcription factor 19 (TCF19) gene has been implicated in conferring a malignant phenotype in cancers. However, its contribution to thyroid neoplasms remains unclear. In this study, we performed genome-wide and phenome-wide association studies to identify a potential causal relationship between TCF19 and thyroid cancer. Our analyses revealed significant associations between TCF19 and various autoimmune diseases and human cancers, including cervical cancer and autoimmune thyroiditis, with a particularly robust signal for the deleterious missense variation rs2073724 that is associated with thyroid function, hypothyroidism, and autoimmunity. Furthermore, functional assays and transcriptional profiling in thyroid cancer cells demonstrated that TCF19 regulates important biological processes, especially inflammatory and immune responses. We demonstrated that TCF19 could promote the progression of thyroid cancer in vitro and in vivo and the C>T variant of rs2073724 disrupted TCF19 protein binding to target gene promoters and their expression, thus reversing the effect of TCF19 protein. Taken together, these findings implicate TCF19 as a promising therapeutic target in aggressive thyroid malignancies and designate rs2073724 as a causal biomarker warranting further investigation in thyroid cancer.

Визуализация в задачах реконструкции данных

Many application tasks of multidimensional data analysis which describe the state of real physical or other systems face with difficulties. This is a consequence of the low-quality source data, including missing values, the probability of errors or unreliability of measurements. Incomplete data can become an obstacle for research using many modern informational methods. The current work examines the potential and capabilities of visual analytics tools for preliminary preparation, correction or complete analysis of primary data volumes. A promising area of application of the approach discussed in the study is the targeted use of visualization capabilities as a data analysis tool. The implementation of specialized visual metaphors is used to solve problems of processing and interpreting data, the sources of which are cyberphysical systems of different complexity levels. Such systems operate in an autonomous or partially controlled mode. A characteristic feature of these systems is the presence of a large number of sensors that collect various types of data. Such data differ in the capacity of the corresponding information channels, their speed and reliability. Examples of such cyberphysical systems are unmanned aerial vehicles (UAVs), robotic stations, and multimodal monitoring systems. These systems can function in conditions where it is difficult to obtain objective observation experience (deep-sea robots). The effective use of data collected by cyberphysical monitoring systems is a condition for solving a large number of application and research tasks.

Health behavior: theoretical and methodical problems of sociological measurement

The article is devoted to a comparative analysis of approaches to the study of health behavior that exist in modern Russian and foreign sociology; identifying theoretical and methodical problems; designation of directions for the development of this issue. The article shows that specific theoretical and methodical problems generated in various approaches can be generalized into two groups. The first correlates with problems of cognitive adequacy of the variables used for analysis. It includes such problems as adapting variables to measure social attitudes or behavior; choosing the optimal way to empirically interpret indicators of health behavior; identifying discrepancies between self-assessments and the results of “objective” measurement. The second group of problems is related to the complexity of the relationships between the components of such behavior, which includes: the mediation of the relationships between the components of health behavior by indicators of a different nature; identification of factors that influence health behavior in different directions, their specificity for various health practices; implementation of conceptual representations in multidimensional data analysis models. The article pays special attention to typological studies as one of the important directions in the development of the problem of sociological measurement of health behavior, which makes it possible to overcome many theoretical and methodical problems. In particular, the advisability of a priori typology of the population being studied is emphasized; testing a number of indicators of health behavior as type-forming attributes; using the experience of conducting typological analysis in subject fields close to the field of health.

A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops.

Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.