Multidimensional Data Analysis Research Articles

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.

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.

Enabling privacy-preserving medication analysis in distributed EHR systems

The digitalization of the medical field has resulted in a vast amount of medical data stored in Electronic Health Record (EHR) systems. Among the various valuable applications of EHRs, medication efficacy analysis stands out as a powerful tool for drug development and enhancing patient treatment. This analysis relies on multi-dimensional medical data, encompassing various factors. Currently, medical research is increasingly inclined towards inter-institutional sharing of medical data to enhance the accuracy and reliability of analysis results. However, due to the sensitivity of personal medical data, privacy and security considerations must be taken into account when conducting medical analyses across institutions. In this paper, we present a novel privacy-preserving scheme for distributed medication efficacy analysis. Our work allows for the analysis of multi-dimensional medical data by leveraging the private set intersection protocol and the Paillier cryptosystem. To validate the feasibility of our scheme, we implement it using some real medical datasets. Experimental results demonstrate that our approach offers enhanced security and lower computational and communication overhead compared to the original approach, which solely processes single-dimensional data at a time.

Quantifying the Influence of Climate Variables on Vegetation Through Remote Sensing and Multi-dimensional Data Analysis

Quantifying the Influence of Climate Variables on Vegetation Through Remote Sensing and Multi-dimensional Data Analysis

Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach

As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance.

Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries/regions, institutions, authors, and scientific collaborations. Results showed that, firstly, the number of articles has grown from 1 in 2002 to 220 in 2022, with a majority being published in interdisciplinary journals that link healthcare and medical research and information technology and AI. Secondly, the significant rise in the quantity of research articles can be attributed to the increasing contribution of scholars from non-English speaking countries/regions and the noteworthy contributions made by authors in the USA and India. Thirdly, researchers show a high interest in diverse research issues, especially, cross-modality magnetic resonance imaging (MRI) for brain tumor analysis, cancer prognosis through multi-dimensional data analysis, and AI-assisted diagnostics and personalization in healthcare, with each topic experiencing a significant increase in research interest. There is an emerging trend towards issues such as applying generative adversarial networks and contrastive learning for multimodal medical image fusion and synthesis and utilizing the combined spatiotemporal resolution of functional MRI and electroencephalogram in a data-centric manner. This study is valuable in enhancing researchers’ and practitioners’ understanding of the present focal points and upcoming trajectories in AI-powered smart healthcare based on multimodal data analysis.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

Circular manufacturing and Industry 5.0. assessing material flows in the manufacturing process in relation to e-waste streams

Abstract The article aims (1) to evaluate material flows in the manufacturing process reflecting the level of circular manufacturing of European Union countries and (2) to estimate the relationship between the level of circular manufacturing and the volume of e-waste put on the market, illustrating the implementation effect of Industry 5.0 technologies. A systematic country classification was created according to development conditions for environmentally sustainable enterprises and trends in e-waste volumes. Multidimensional data analysis and the linear ordering method were used to achieve the research objectives. The dynamics of changes in the identified variables were analysed using dynamics indexes and the average annual rate of change. Relationships were estimated using Pearson’s linear correlation coefficient. The main research result is the estimated synthetic development measure illustrating the level of circular manufacturing in the context of material flows. Significant differences were observed between the synthetic development measure values representing the level of circular manufacturing in European Union countries. This means countries’ circular manufacturing levels are significantly higher than others. Moreover, the values of correlation coefficients were estimated between the level of circular manufacturing and the volume of e-waste put on the market and between the average annual rate of change of the synthetic development measure and the average annual rate of change of the e-waste volume. The coefficient values do not confirm a statistically significant relationship between the indicated variables. Most countries have average conditions for developing environmentally sustainable businesses, but at the same time, they show negative trends in the volume of e-waste generated.

Phenotypic Analysis of Hematopoietic Stem and Progenitor Cell Populations in Acute Myeloid Leukemia Based on Spectral Flow Cytometry, a 20-Color Panel, and Unsupervised Learning Algorithms.

The analysis of hematopoietic stem and progenitor cell populations (HSPCs) is fundamental in the understanding of normal hematopoiesis as well as in the management of malignant diseases, such as leukemias, and in their diagnosis and follow-up, particularly the measurement of treatment efficiency with the detection of measurable residual disease (MRD). In this study, I designed a 20-color flow cytometry panel tailored for the comprehensive analysis of HSPCs using a spectral cytometer. My investigation encompassed the examination of forty-six samples derived from both normal human bone marrows (BMs) and patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) along with those subjected to chemotherapy and BM transplantation. By comparing my findings to those obtained through conventional flow cytometric analyses utilizing multiple tubes, I demonstrate that my innovative 20-color approach enables a more in-depth exploration of HSPC subpopulations and the detection of MRD with at least comparable sensitivity. Furthermore, leveraging advanced analytical tools such as t-SNE and FlowSOM learning algorithms, I conduct extensive cross-sample comparisons with two-dimensional gating approaches. My results underscore the efficacy of these two methods as powerful unsupervised alternatives for manual HSPC subpopulation analysis. I expect that in the future, complex multi-dimensional flow cytometric data analyses, such as those employed in this study, will be increasingly used in hematologic diagnostics.

Time Series Feature Selection Method Based on Mutual Information

Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.