Data Processing Strategies Research Articles

Novel PFAS-specific monitoring approach for highly impacted surface waters

In regulatory environmental monitoring programs, only a very small fraction of the vast number of per- and polyfluoroalkyl substances (PFAS) are investigated by target analysis. Therefore, non-target analysis (NTA) studies are increasingly conducted to detect unknown or unnoticed PFAS. These studies are often based on a few grab samples. Thus, discontinuously emitted PFAS from industrial batch processes might be easily overlooked. To address this deficiency and obtain in-depth information on the occurrence and temporal trend of PFAS in surface water impacted by treated industrial waste water, a comprehensive target and NTA study was implemented for 29 months.Elevated PFAS concentrations with up to 10.8 μg L−1 were detected in the river water by target analysis. In addition to PFAS target analysis, the water samples were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). Data processing strategies and various filtering steps were applied to prioritize PFAS. Substances were identified by comparing data to available internal and external PFAS suspect lists, a fragment ion and neutral loss list, and spectral libraries. Several compounds were unequivocally identified based on reference standards.Fifty-five PFAS were (tentatively) identified using NTA. Of those, 43 could be assigned to 13 different homologous series. Partly fluorinated short-chain carboxylic acids (H-PFCA) and sulfonic acids (H-PFSA) were predominantly found in addition to perfluoroalkyl carboxylic acids (PFCA) and the alkyl ether carboxylic acid DONA. To the best of our knowledge, 12 PFAS were reported in surface water for the first time. Signal intensities of individual PFAS and signal ratios varied widely over time, which may indicate batch operations leading to discontinuous emission. Results and insights from this screening approach on PFAS can be used to optimize forthcoming surface water monitoring programs by including newly identified PFAS and selecting appropriate sampling intervals.

Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients.

Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.

DIR3D: Cascaded Dual-Domain Inter-Scale Mutual Reinforcement 3D Network for highly accelerated 3D MR image reconstruction

Deep Learning has been successfully applied to reconstruct Magnetic Resonance (MR) images from undersampled k-space data to achieve the acceleration of MRI. However, most existing works focused on 2D MRI scans and perform reconstruction in a slice-by-slice manner due to the tremendous computation and memory cost of 3D MRI reconstruction, resulting in a blank in the research of 3D MRI acceleration. Providing more accurate and diagnostic information, 3D MRI requires excessively long scan time. To accelerate 3D MRI, in this work, we design a lightweight and powerful cascaded 3D network DIR3D utilizing a previously proposed computation-friendly data processing strategy. Specifically, we propose an efficient block called Dual-Domain Inter-Scale Mutual Reinforcement Block (DIRB) to fuse multi-scale features locally and globally with neglectable computation and memory costs, which enhances the representative ability of the network. To allow more flexibility, we further redesign the commonly used Data Consistency (DC) layer by introducing a learnable adaptor which enables the network to perform point-wise adaptive merge of the reconstructed and sampled k-space data while ensuring data consistency. We conduct comprehensive experiments on the Stanford MRIData and evaluate our DIR3D from multiple perspectives. When achieving the same acceleration factors, our proposed DIR3D consistently outperforms other state-of-the-art 2D methods at multiple subsampling masks, especially for highly undersampled data, which provides strong evidence for the superiority of our DIR3D for 3D MRI acceleration. Additionally, at the inference stage, our DIR3D can achieve a much higher reconstruction efficiency.

The importance of molecular axis alignment and symmetry-breaking in photoelectron elliptical dichroism.

Photoelectron angular distributions (PADs) produced from the photoionization of chiral molecules using elliptically polarized light exhibit a forward/backward asymmetry with respect to the optical propagation direction. By recording these distributions using the velocity-map imaging (VMI) technique, the resulting photoelectron elliptical dichroism (PEELD) has previously been demonstrated as a promising spectroscopic tool for studying chiral molecules in the gas phase. The use of elliptically polarized laser pulses, however, produces PADs (and consequently, PEELD distributions) that do not exhibit cylindrical symmetry about the propagation axis. This leads to significant limitations and challenges when employing conventional VMI acquisition and data processing strategies. Using novel photoelectron image analysis methods based around Hankel transform reconstruction tomography and machine learning, however, we have quantified-for the first time-significant symmetry-breaking contributions to PEELD signals that are of a comparable magnitude to the symmetric terms in the multiphoton ionization of (1R,4R)-(+)- and (1S,4S)-(-)-camphor. This contradicts any assumptions that symmetry-breaking can be ignored when reconstructing VMI data. Furthermore, these same symmetry-breaking terms are expected to appear in any experiment where circular and linear laser fields are used together. This ionization scheme is particularly relevant for investigating dynamics in chiral molecules, but it is not limited to them. Developing a full understanding of these terms and the role they play in the photoionization of chiral molecules is of clear importance if the potential of PEELD and related effects for future practical applications is to be fully realized.

3d model of Medova cave, Lviv

The purpose of this article is to present and provide a detailed analysis of the 3D model of the Medova Cave in Lviv, created using laser scanning technology. The main objectives include revealing the accuracy and detailing the obtained model, as well as determining its potential applications in various fields such as geological research, scientific exploration, cultural heritage, and tourism. Additionally, the article aims to emphasize the importance and advantages of using laser scanning for creating precise and realistic 3D cave models, opening new possibilities for the study and preservation of natural unique formations. Method. The article describes the methodology used to create a 3D model of the Medova Cave, which involves several key steps. Firstly, a thorough reconnaissance of the cave is conducted to identify technical and logistical aspects such as temperature, humidity, movement restrictions, and lighting that are necessary for the scan. Next, ground-based laser scanning is employed, proving effective in conditions of complete darkness and limited space, to ensure accurate data collection regarding the cave's geometry. Reflective markers are strategically placed inside the cave before scanning, facilitating efficient scanning and alignment of scans. Specialized equipment such as GNSS receivers (Trimble R7) and ground-based laser scanners (Faro Focus 3D 120) are utilized for data collection. Software tools like Faro Scene are used for stitching together scans into a unified 3D point cloud model during data processing. Finally, the accuracy of marker connections is analyzed to ensure high-quality registration, and the 3D model is constructed. Detailed 3D models, including textured models and cross-sections for visualizing the internal structure of the cave, were constructed using software such as Move. Results. The research on the Medova Cave, employing ground-based laser scanning, yielded an accurate and detailed 3D model of the cave. This model opens new perspectives for geological and geomorphological studies, tourism development, and cultural heritage preservation. The use of advanced scanning technologies allowed for a comprehensive representation of the cave's geometric features, considering its complex structure and varied dimensions. Scientific novelty and practical significance. The creation of the 3D model of the Medova cave using laser scanning signifies progress in cave geometry studies. The innovation lies in the utilization of advanced laser scanning technologies to ensure a detailed representation of the cave's geometric features, accounting for its complex structure and diverse dimensions. The novelty also lies in the development of a data collection and processing strategy in conditions of complete darkness and limited cave space, resulting in an accurate and realistic 3D model. Practically, the 3D model of the Medova Cave serves as a crucial tool for geological and geomorphological research and the exploration of unique natural formations. The model opens new opportunities for tourism development, where virtual cave exploration can provide a unique experience for visitors. Creating this 3D model is a significant step in preserving and documenting cultural heritage, contributing to the scientific and cultural development of the region, and providing access to unique objects for researchers and the public.

Diagnostics of Concrete Box Beam Bridges Using Wireless Sensors and Finite Element Analysis

Abstract Three pretensioned adjacent concrete box beam bridges were studied with a structural health monitoring (SHM) paradigm based on strain measurements and finite element static analyses. An accurate model for one bridge and an approximate model for the other two were created using ansys software. The analyses were used to calculate the strains generated by six concentrated loads that mimic the presence of a truck. Pristine and damage scenarios were implemented, and the associated numerical strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test. As the results from the approximate models deviated significantly from the field response of the bridge, the accurate model applied to one bridge was extended to the other two. The comparison between numerical and experimental results revealed the presence of noncritical anomalies related to strain distribution across adjacent beams. Such issues were confirmed with the examination of the historical strains streamed for several months to a repository, using simple data processing strategies. The intellectual contribution of the work resides in the combination of finite element analysis and SHM applied to three existing bridges with very similar structural characteristics. This combination revealed the presence of noncritical issues impossible to be diagnosed with conventional inspection.

Optimizing Data Processing and Security in Edge Computing For Iot Applications

Edge computing has emerged as a pivotal paradigm in the realm of Internet of Things (IoT) applications, addressing challenges of latency, bandwidth, and reliability by decentralizing computation and storage closer to the data source. This paper explores the critical aspects of optimizing data processing and enhancing security in edge computing environments to foster efficient and secure IoT deployments. In recent years, the proliferation of IoT devices has led to an exponential growth in data generation at the edge. Traditional cloud-centric approaches struggle to cope with the sheer volume and velocity of data produced by these devices, often resulting in latency issues and increased operational costs. Edge computing offers a promising solution by enabling data processing and analytics closer to where data is generated, thereby reducing latency and bandwidth consumption. The first part of this paper reviews the foundational concepts of edge computing, emphasizing its architectural principles and distinguishing characteristics in contrast to centralized cloud computing. Key optimization strategies for data processing at the edge are discussed, including edge analytics, data filtering, and compression techniques. These strategies aim to minimize data transmission to centralized cloud servers, thereby enhancing operational efficiency and real-time responsiveness in IoT applications.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Optimizing UAV computation offloading via MEC with deep deterministic policy gradient

AbstractMobile edge computing (MEC) seems to be highly efficient to process the generated data from IoT devices by providing computational resources locating in close range to network edge. MEC can be promising in reduction of latency and consumption of energy from data transmissions from offloading computational tasks from IoT devices to nearby edge servers. In the context of the growing IoT ecosystem, there is an increasing need for efficient data processing and communication strategies. There is a demand of bridging the gap in current research with novel optimization algorithms tailored for UAV‐assisted MEC systems, shedding light on the necessity of efficient computation offloading in meeting the demands of the IoT era. In this article, a computation offloading optimization algorithm is proposed which is based on deep deterministic policy gradient for realistic Aurelia X6 Pro unmanned aerial vehicle (UAV)‐assisted MEC systems. The proposed algorithm optimizes the offloading decision for UAVs by taking task characteristics and the communication environment into consideration. To demonstrate the effectiveness of the proposed algorithm, comprehensive simulations were conducted, and the results indicate substantial improvements in MEC systems' competency. Our research not only showcases the feasibility of deep deterministic policy gradient in UAV‐assisted MEC systems but also highlights the importance of developing efficient computation offloading strategies for the evolving landscape of IoT and edge computing.

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph.

The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.

Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies

The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1st derivative, 2nd derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin.

A high-precision and transparent step-wise diagnostic framework for hot-rolled strip crown

The strip crown plays a crucial role in determining the quality of products in strip hot rolling. Machine learning (ML) methods have shown promise in crown prediction by effectively capturing the nonlinearities and strong coupling present in hot rolling data, surpassing the capabilities of traditional methods. However, existing ML models ignore the imbalance of strip crown and tend to prioritize learning information from the qualified crown, limiting the precision of diagnosing the faulty crown. To overcome this limitation, a novel high-precision step-wise diagnostic framework is proposed. The framework starts with a crown detection module that promptly detects the faulty crown and enables timely blocking of the faulty strip. To enhance the diagnostic precision for the faulty crown, a novel hybrid data processing strategy that combines resampling method and cost-sensitive learning is introduced within the detection module, and the cost factor is optimized by Chaotic Harris Hawks Optimizer (CHHO). Subsequently, the framework incorporates a crown classification module to accurately recognize the specific fault-type present in the faulty strip. Furthermore, eXplainable Artificial Intelligence (XAI) technique is employed to ensure the transparent decision-making processes of both the detection module and the classification module. The comparative experiment results demonstrate that the proposed framework outperforms other state-of-the-art ML methods. It can achieve an excellent trade-off between precision and efficiency in diagnosing hot-rolled strip crown. Additionally, the feature contributions and decision interpretable analysis based on XAI provide further evidence of the transparency and effectiveness of the proposed framework.

Oral processing, rheology, and mechanical response: Relations in a two-phase food model with anisotropic compounds.

Food-material poses a challenging matrix for objective material scientific description that matches the consumers' perception. With eyes on the emerging structured food materials from alternative protein sources, objectively describing perceived texture characteristics became a topic of interest to the food industry. This work made use of the well-known methodologies of jaw tracking and electromyography from the field of "food oral processing" and compared outcomes with mechanical responses to the deformation of model food systems to meat alternatives. To enable transferability to meat alternative products, an anisotropic structuring ingredient for alternative products, high-moisture texturized vegetable protein (HM-TVP), was embedded in an isotropic hydrocolloid gel. Data of the jaw movement and muscle activities exerted during mastication were modeled in a linear mixed model and set in relation to characteristic values obtained from small- and large-strain deformation. For improvement of the model fit, this work makes use of two new data-processing strategies in the field of oral processing: (i) Muscle activity data were set in relation to true forces and (ii) measured data were standardized and subjected to dimensional reduction. Based on that, model terms showed decreased p-values on various oral processing features. As a key outcome, it could be shown that an anisotropic structured phase induces more lateral jaw movement than isotropic samples, as was shown in meat model systems.

In-depth proteome analysis of brain tissue from Ewsr1 knockout mouse by multiplexed isobaric tandem mass tag labeling

EWS RNA binding protein 1 (EWSR1) is a multifunctional protein whose epigenetic signatures contribute to the pathogenesis of various human diseases, such as neurodegenerative disorders, skin development, and tumorigenic processes. However, the specific cellular functions and physiological characteristics of EWSR1 remain unclear. In this study, we used quantitative mass spectrometry-based proteomics with tandem mass tag labeling to investigate the global proteome changes in brain tissue in Ewsr1 knockout and wild-type mice. From 9115 identified proteins, we selected 118 differentially expressed proteins, which is common to three quantitative data processing strategies including only protein level normalizations and spectrum-protein level normalization. Bioinformatics analysis of these common differentially expressed proteins revealed that proteins up-regulated in Ewsr1 knockout mouse are mostly related to the positive regulation of bone remodeling and inflammatory response. The down-regulated proteins were associated with the regulation of neurotransmitter levels or amino acid metabolic processes. Collectively, these findings provide insight into the physiological function and pathogenesis of EWSR1 on protein level. Better understanding of EWSR1 and its protein interactions will advance the field of clinical research into neuronal disorders. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026994.

Unambiguous detection of SARS-CoV-2 subgenomic mRNAs with single-cell RNA sequencing.

Single-cell RNA sequencing (scRNA-Seq) studies have provided critical insight into the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). scRNA-Seq library preparation methods and data processing workflows are generally designed for the detection and quantification of eukaryotic host mRNAs and not viral RNAs. Here, we compare different scRNA-Seq library preparation methods for their ability to quantify and detect SARS-CoV-2 RNAs with a focus on subgenomic mRNAs (sgmRNAs). We show that compared to 10X Genomics Chromium Next GEM Single Cell 3' (10X 3') libraries or 10X Genomics Chromium Next GEM Single Cell V(D)J (10X 5') libraries sequenced with standard read configurations, 10X 5' libraries sequenced with an extended length read 1 (R1) that covers both cell barcode and transcript sequence (termed "10X 5' with extended R1") increase the number of unambiguous reads spanning leader-sgmRNA junction sites. We further present a data processing workflow, single-cell coronavirus sequencing (scCoVseq), which quantifies reads unambiguously assigned to viral sgmRNAs or viral genomic RNA (gRNA). We find that combining 10X 5' with extended R1 library preparation/sequencing and scCoVseq data processing maximizes the number of viral UMIs per cell quantified by scRNA-Seq. Corresponding sgmRNA expression levels are highly correlated with expression in matched bulk RNA-Seq data sets quantified with established tools for SARS-CoV-2 analysis. Using this scRNA-Seq approach, we find that SARS-CoV-2 gene expression is highly correlated across individual infected cells, which suggests that the proportion of viral sgmRNAs remains generally consistent throughout infection. Taken together, these results and corresponding data processing workflow enable robust quantification of coronavirus sgmRNA expression at single-cell resolution, thereby supporting high-resolution studies of viral RNA processes in individual cells. IMPORTANCE Single-cell RNA sequencing (scRNA-Seq) has emerged as a valuable tool to study host-virus interactions, especially for coronavirus disease 2019 (COVID-19). Here we compare the performance of different scRNA-Seq library preparation methods and sequencing strategies to detect SARS-CoV-2 RNAs and develop a data processing workflow to quantify unambiguous sequence reads derived from SARS-CoV-2 genomic RNA and subgenomic mRNAs. After establishing a workflow that maximizes the detection of SARS-CoV-2 subgenomic mRNAs, we explore patterns of SARS-CoV-2 gene expression across cells with variable levels of total viral RNA, assess host gene expression differences between infected and bystander cells, and identify non-canonical and lowly abundant SARS-CoV-2 RNAs. The sequencing and data processing strategies developed here can enhance studies of coronavirus RNA biology at single-cell resolution and thereby contribute to our understanding of viral pathogenesis.

A sustainable data processing approach using ultraviolet-spectroscopy as a powerful spectral resolution tool for simultaneously estimating newly approved eye solution in the presence of extremely carcinogenic impurity aided with various greenness and whiteness assessment perspectives: Application to aqueous humor

Drug impurities are now seen as a major threat to drug safety around the world, especially when it comes to carcinogenic impurities. Here, we present the first spectrophotometric approach for the quantification of lignocaine and fluorescein in the presence of 2,6-xylidine, lignocaine’s carcinogenic impurity. The approach depends on overcoming unresolved bands through a data processing strategy employing 10 affordable, simple, and sensitive spectrophotometric methods. Fluorescein analysis (1–16 μg mL−1) was performed using direct ultraviolet spectrophotometry (D0) at 478 nm; then, the ratio subtraction method allowed the removal of interference caused by the fluorescein spectrum. From the resulting ratio spectra, 2,6-xylidine (40–160 μg mL−1) can be efficiently determined at 280 nm. However, lignocaine (72–320 μg mL−1) was analyzed using different ultraviolet-based methods, including continuous wavelet transform, ratio derivative by Savitzky–Golay filters, mean centering, second derivative of ratio spectra, ratio spectra difference spectrophotometry, extended ratio subtraction, absorbance subtraction, Q-absorbance ratio, and area under the curve. In line with International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH), the presented approach was evaluated by its coefficients of determination, detection limits, quantification limits, and relative standard deviations. Similarly, the developed approach was assessed for whiteness, greenness, and sustainability using five assessment tools, including National Environmental Method Index, Eco-Scale Assessment, Complementary Green Analytical Procedure Index, Analytical Greenness Metric, and RGB12, offering promising results. Owing to the satisfactory analytical performance, besides the sustainability, affordability, simplicity, and cost efficiency of the presented methods, their application for quality control and in situ analysis in minimal-infrastructure laboratories increases, increasing the surveillance potential.

An analytic strategy for data processing of multimode networks

Complex network data structures are considered to capture the richness of social phenomena and real-life data settings. Multipartite networks are an example in which various scenarios are represented by different types of relations, actors, or modes. Within this context, the present contribution aims at discussing an analytic strategy for simplifying multipartite networks in which different sets of nodes are linked. By considering the connection of multimode networks and hypergraphs as theoretical concepts, a three-step procedure is introduced to simplify, normalize, and filter network data structures. Thus, a model-based approach is introduced for derived bipartite weighted networks in order to extract statistically significant links. The usefulness of the strategy is demonstrated in handling two application fields, that is, intranational student mobility in higher education and research collaboration in European framework programs. Finally, both examples are explored using community detection algorithms to determine the presence of groups by mixing up different modes.

An automatic preselection strategy for magnetotelluric single-site data processing based on linearity and polarization direction

The magnetotelluric response function can be severely disturbed by cultural electromagnetic noise. The preselection strategy is one of the effective ways to remove the influence of noise when calculating the response function. This study proposed three new parameters (the amplitude ratio predicted amplitude ratio and linear coherence (PLcoh) between the predicted and observed electric fields and the dispersion degree of the magnetic polarization direction (DDpol)) to detect noisy data, making the preselection strategy automatic. The first two were used to evaluate the linearity of binary linear regression to constrain incoherent noise, while the last was used to evaluate the magnetic polarization direction to constrain coherent noise. Finally, the technique is illustrated by applying it to two field datasets and comparing it with the previous studies. The results showed that these parameters can be used to effectively identify contaminated data, and a reliable response function can be obtained by using these parameters to extract high-quality data when intermittent noise contaminates field data.

Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation.

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.

Elastic modulus identification of particles in particulate composite through nanoindentation

The characterization of the constitutive behavior of embedded particles is crucial for the mechanical analysis of functional particulate composites. However, current methods for identifying the elastic parameters through instrumented indentation tests lack robust theoretical foundations. In this study, a series-connection analytical model was proposed to determine the Young's modulus of the embedded particles based on the Oliver-Pharr (O-P) model. Additionally, a simplified regression function was derived from the complete analytical model.To validate the accuracy of these methods in estimating the Young's modulus, 2D indentation simulations were conducted. Furthermore, in order to facilitate practical engineering applications, a data processing strategy was developed and implemented to determine the statistical value of the Young's modulus for LiNixMnyCozO2 (NMC) particles in the cathode of a Lithium-ion battery (LIB). Moreover, data obtained from continuous stiffness measurement (CSM) of a single embedded NMC particle were utilized to verify the reliability of the simplified regression function. The simplified regression function was found to possess significant engineering potential in related studies, as it enables a quick and reliable approximation of the Young's modulus of embedded particles without requiring detailed experimental information.