Data Matrix Research Articles

Customizable orodispersible films: Inkjet printing and data matrix encoding for personalized hydrocortisone dosing

The aim of this study was to exploit the versatility of inkjet printing to develop flexible doses of drug-loaded orodispersible films that encoded information in a data matrix pattern, and to introduce a specialised data matrix-generator software specifically focused on the healthcare sector. Pharma-inks (drug-loaded inks) containing hydrocortisone (HC) were developed and characterised based on their rheological properties and drug content. Different strategies were investigated to improve HC solubility: formation of β-cyclodextrin complexes, Soluplus® based micelles, and the use of co-solvent systems. The software automatically adapted the data matrix size and identified the number of layers for printing. HC content deposited in each film layer was measured, and it was found that the proportion of co-solvent used directly affected the drug solubility and simultaneously played a role in the modification of the viscosity and surface tension of the inks. The formation of β-cyclodextrin complexes improved the drug quantity deposited in each layer. On the contrary, micelle-based inks were not suitable for printing. Orodispersible films containing flexible and low doses of personalised HC were successfully prepared, and the development of a code generator software oriented to medical use provided an additional, innovative, and revolutionary advantage to personalised medicine safety and accessibility.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application.

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model. Multiblock SIMCA is shown to be useful for variable selection, and Tucker 3 SIMCA to select the optimal model complexity when applying multiway data decomposition and to assess the role of individual samples in the classification model. Both approaches, together with the two-way DD-SIMCA version applied to the unfolded data, are compared regarding the analysis of an experimental data set including genuine and adulterated blueberry extract samples. The latter were employed to produce matrix spectral-time data matrices per sample within a flow injection system, taking advantage of the spectral changes in the sample constituents as a function of the pH of the carrier phase. The need to employ the Tucker 3 model instead of a trilinear decomposition is supported by a discussion on the lack of the trilinearity property of the studied data.

Degradative Quality and Emission Implications of Repeatedly Heated Cooking Oil in Indoor Environment

The study examined the effects of heating the same batch of oil for multiple frying cycles on the pollutants that are released into the air and the degradation of the oil’s quality. Principal component analysis under the physicochemical metrics framework was used to study singular and interaction effects of three oil brands, frying duration and temperature. Physicochemical parameters showed that peanut oil has high unsaponifiable matters, the refractive index and kinetic viscosity prior to frying. Specific gravity and acid value were significantly lower for soya bean oil after frying with values 0.42 ± 0.02 and 0.20 ± 0.01 in order. The values of 4.09 ± 0.04 meqO2/kg and 114.96 ± 0.23 mgKOH/g obtained for peroxide value and saponification value respectively were significant for olive oil during post frying analysis. The emission values were in the ranges 98.17 ± 0.15 (x103)-573.20 ± 0.20 (x103),11.0 mg/m3 at 60 °C-34.0 mg/m3 at 140 °C, 0.051 mg/m3 at 60 °C-0.088 mg/m3 release at 140 °C, 59.0 μg/m3 at 80 °C-72.0 μg/m3 at 120 °C, and 0.057 mg/m3 at 60 °C-0.078 mg/m3 at 140 °C respectively for PAH, CO, NO2, SPM, SO2. Temperature had a significant impact on the rates of emissions when combined with time. The inherent patterns and trends within the data matrix suggest that oil brand quality was the most significant factor influencing the clustering of observations. Therefore, heating cooking oils to moderate temperatures and using a large volume of the oil could result in minimal rates of evaporation and pollutants emission

Economic review of the tariff liberalization of the import of wood and wood products: Evidence from a developing country

In recent years, import tariff liberalization of wood and wood products has attracted the attention of developing countries; especially in countries with sharp decrease in the forest green areas. However, due to the increase in demand for wood products in developing countries and the limitation of internal resources in these countries, the import tariff is very important. On the other hand, increasing the import tariff of wood to meet the domestic demand of each country and reducing the pressure on the forests could be effective. Therefore, in this article, the effects of reducing the tariff on wood and wood products on GDP, import of economic sectors and domestic production of wood and wood products, and followed pressure on the forests of a developing country (Iran) is examined based on a computable general equilibrium model. This model is designed based on micro data matrix of 2017 in Iran which faced with reduced forest size. Our results showed that reduction in the import tariff of wood and wood products, cause increases the import in this sector, gross national product, and prosperity. It also reduces the pressure on forests in the country. Therefore, we recommend that other countries that are facing the reduction of forest green areas, reduce the wood import tariff based on a specific time schedule so that help the growth of forest green areas in susceptible and needed areas which in turn help to improve the condition of the planet.

Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training. The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation. We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry. We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): . Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.

STNet: A Time-Frequency Analysis-Based Intrusion Detection Network for Distributed Optical Fiber Acoustic Sensing Systems.

Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space-time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.

Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and thermal emission data

With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic sensing, photodiode sensing, and high-speed imaging. However, real-time acquisition of high-resolution melt pool images in particular remains computationally demanding in practice due to the high variability of melt pool morphologies and the limitation of data caching and transfer, making it challenging to detect the local lack-of-fusion (LOF) defect occurrences. In this work, we propose a new acoustic and thermal information-based monitoring method that can robustly infer critical LPBF melt pool morphological features in image forms and detect spatially-dependent LOF defects within a short time period. We utilize wavelet scalogram matrices of acoustic and photodiode clip data to identify and predict highly time-resolved (within a 1.0 ms window) visual melt pool characteristics via a well-trained data-driven pipeline. With merely the acoustic and photodiode-collected thermal emission data as the input, the proposed pipeline enables data-driven inference and tracking of highly variable melt pool visual characteristics with R2≥0.8. We subsequently validate our proposed approach to infer local LOF defects between two adjacent scanlines, showing that our proposed approach can outperform our selected baseline theoretical model based on previous literature. Revealing the physical correlation between airborne acoustic emission, thermal emission, and melt pool morphology, our work demonstrates the feasibility of creating an efficient and cost-effective acoustic- and thermal-based approach to facilitate online visual melt pool characterization and LOF defect detection. We believe that our work can further contribute to the advances in quality control for LPBF.

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation.

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Validation and Adaptation of Questionnaires on Interest, Effort, Progression and Learning Support in Chilean Adolescents

In order to understand interest, effort and progress in learning as dispositional and contextual variables in the field of education, the activities and strategies that encourage student motivation have been continuously sought, given that they have a fundamental role in sustainability to promote the improvement of their citizenship skills and the achievement of SDGs 3 (health and well-being) and 4 (quality education) set by the UN. The objective of this study is to validate and adapt the Interest, Effort and Progress in Learning (IEPA) and contextual Student Assistance (AYES) questionnaires in the Chilean adolescent population and thus support the sustainable development SDGs. For this purpose, they were applied to a sample in two phases, first with 339 schoolchildren, and secondly, replicated with 3172 students. For their analysis, a data matrix was constructed with distribution and dispersion tests (mean, standard deviation, skewness, kurtosis and range) using the IBM SPSS.27 statistical program. Subsequently, the dimensionality of the scale was studied by applying an exploratory factor analysis with the FACTOR program, version 11, updated in 2021. Finally, a confirmatory factor analysis was performed with the M-PLUS.7.3 program. It is concluded that the instruments provide a method that is valid, reliable, simple to apply and adapted to adolescents, allowing the evaluation of three dispositional variables in students: interest, effort and progression in learning. These data indicate that they have adequate psychometric properties, which allows for a valid and reliable evaluation to contribute to the sustainability of permanent improvement in education.

Massively parallel implementation of iterative eigensolvers in large-scale plane-wave density functional theory

The Kohn-sham density functional theory (DFT) is a powerful method to describe the electronic structures of molecules and solids in condensed matter physics, computational chemistry and materials science. However, large and accurate DFT calculations within plane waves process a cubic-scaling computational complexity, which is usually limited by expensive computation and communication costs. The rapid development of high performance computing (HPC) on leadership supercomputers brings new opportunities for developing plane-wave DFT calculations for large-scale systems. Here, we implement parallel iterative eigensolvers in large-scale plane-wave DFT calculations, including Davidson, locally optimal block preconditioned conjugate gradient (LOBPCG), projected preconditioned conjugate gradient (PPCG) and the Chebyshev subspace iteration (CheFSI) algorithms, and analyze the performance of these algorithms in massively parallel plane-wave computing tasks. We adopt a two-level parallelization strategy that combines the message passing interface (MPI) with open multi-processing (OpenMP) parallel programming to handle data exchange and matrix operations in the construction and diagonalization of large-scale Hamiltonian matrix within plane waves. Numerical results illustrate that these iterative eigensolvers can scale up to 42,592 processing cores with high peak performance of 30% on leadship supercomputers to study the electronic structures of bulk silicon systems containing 10,648 atoms. Program summaryProgram Title: Plane wave density functional theory (PWDFT)CPC Library link to program files:https://doi.org/10.17632/c8v2mx5vn4.1Developer's repository link:https://bitbucket.org/berkeleylab/scalesLicensing provisions: BSD 3-clauseProgramming language: C++Nature of problem: PWDFT is used for electronic structure calculations based on Kohn-Sham density functional theory. The key challenge to address is a constrained energy minimization problem, which can also be formulated as a nonlinear eigenvalue problem. MPI/OpenMP-based approaches are employed to provide multi-core acceleration for the study of the chemical and material properties of larger-scale molecules and solids.Solution method: PWDFT implements self-consistent field (SCF) iterations and direct constrained minimization algorithms with various acceleration strategies. It is written in C++ and offers parallel acceleration based on MPI/OpenMP.

T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE , a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.

Multi-view subspace clustering based on adaptive search

Multi-view clustering has been widely applied to image classification, information retrieval, medical pathology analysis, and other fields. So far, many multi-view subspace clustering algorithms based on self-representation learning have been developed. However, most of them use the original data as a dictionary to construct affinity graphs, and their clustering performance depends largely on the quality of the original data features. If the raw data is corrupted by noise, directly using these noisy data as a self-representing dictionary may pose a series of research challenges. Secondly, in the process of self-representation learning, the internal relationship between data points is actually continuously updated and changed, and fixed data points not only affect the selection of view feature diversity, but also may lead to clustering results that are unusually sensitive to the input data. Finally, the coefficient matrix obtained by self-representation learning may contain noise or data structures that are not relevant to multi-view clustering. To remedy these shortcomings, this work proposes a new multi-view clustering algorithm, namely, multi-view subspace clustering based on adaptive search. Specifically, first, we find its similar data matrix for each view’s original data and perform dictionary learning by using the similar data matrix as a dictionary. This not only facilitates identifying the continuously varied internal relationships between data points, but also alleviates the errors that occur when the raw data is directly selected as a dictionary. Second, we introduce robust principal component analysis (RPCA) and rank constraints into the construction of affinity matrices to obtain cleaner and more robust affinity matrices that explore the same clustering properties among different views. In addition, we develop an augmented Lagrange multiplier (ALM) based method to solve the objective function of the model. Finally, we conducted substantial experiments on six real multi-view datasets to demonstrate that the MSC-AS algorithm is more robust and effective than some other advanced clustering algorithms.

Extended eigenvalue calibration method for overdetermined Mueller matrix polarimeters.

The eigenvalue calibration method is a versatile approach that can be applied to any type of the Mueller matrix polarimetic setup because a precise knowledge of the optical response of the setup components is not required. The method has usually been employed in its original form to calibrate non-overdetermined polarimeters dealing with intensity data arranged in 4 × 4 matrices, but it can also be applied to calibrate overdetermined polarimeters with intensity data matrices of higher dimension. The main drawback with the original formulation of the method is its sensitivity to noise in the input data, especially if applied as it is to overdetermined intensity matrices. In the present work, we present a rigorous extension of the conventional eigenvalue calibration method to treat overdetermined data. We experimentally show that the proposed method does not enhance noise propagation, and therefore it does not degrade the quality of Mueller matrices measured with overdetermined polarimeters.

Small mammal records from Limay river basin (Northwestern Patagonia) in the Anthropocene from a taphonomical and paleoecological perspective

The studies of the small mammal fossil and recent assemblages from the Limay river basin (Northwestern Patagonia) have strongly increased during the last decade. Taxonomic, taphonomic, and paleoecological information about small rodents and marsupials recovered from fossil sites offers the opportunity to discuss the periods of change and stability in the conformation of their communities through the Anthropocene. Here, we used two large data matrix of fossil and recent small mammal samples. As starting point, we considered the small mammal record of Epullán Grande cave (LL thereafter), which covers the Early Holocene/Post-hispanic Period, in order to assess the impact of anthropic activities on the small mammal communities during the Anthropocene. The taphonomic analysis performed on the newest samples from LL confirms the predatory activity of Tyto furcata on sigmodontines and human consumption on caviomorphs (mostly for the last ca. 1000 years). The analysis of manganese oxide staining suggested higher levels of moisture during the earliest formation of the LL sequence. The taxonomic results indicate a major diversity in the small mammal fossil assemblages to the later periods of LL and other fossil sequences of the Limay basin of the Anthropocene. Conversely, opportunistic sigmodontines ( Abrothrix olivacea, Calomys musculinus, Eligmodontia spp. and Oligoryzomys longicaudatus) experienced a growth in the recent samples. Some stenoic and specialist species ( Euneomys spp., Lestodelphys halli, Loxodontomys micropus and Reithrodon auritus) were abundant in the temporal units associated with the Anthropocene, but now are in retraction. Additionally, the drop in the diversity of recent assemblages supports a restructuration of small mammal communities from Limay river basin occurred in the 20th century.

The Growth of Contemporary Music Subject and the Reform of Music Teaching in Universities

In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of music teaching resources in colleges and universities. The test results show that the accuracy of the proposed teaching resource recommendation algorithm is improved by 22.56% compared with the traditional CF algorithm. The improved CF algorithm can provide more accurate prediction, and the recommendation effect of the improved algorithm is better than the original algorithm, which can effectively avoid the sparse matrix problem faced by the CF algorithm, and provide technical support for the development of contemporary music discipline and the reform of music discipline.

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Community structure is a significant characteristic of complex networks, and community detection has valuable applications in network structure analysis. Non-negative matrix factorization (NMF) is a key set of algorithms used to solve the community detection issue. Nevertheless, the localization of feature vectors in the adjacency matrix, which represents the characteristics of complex network structures, frequently leads to the failure of NMF-based approaches when the data matrix has a low density. This paper presents a novel algorithm for detecting sparse network communities using non-negative matrix factorization (NMF). The algorithm utilizes local feature vectors to represent the original network topological features and learns regularization matrices. The resulting feature matrices effectively reveal the global structure of the data matrix, demonstrating enhanced feature expression capabilities. The regularized data matrix resolves the issue of localized feature vectors caused by sparsity or noise, in contrast to the adjacency matrix. The approach has superior accuracy in detecting community structures compared to standard NMF-based community detection algorithms, as evidenced by experimental findings on both simulated and real-world networks.

A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses.

Grade of membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore, GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM comes at the cost of more challenging identifiability and estimation problems. In this work, we propose a singular value decomposition (SVD)-based spectral approach to GoM analysis with multivariate binary responses. Our approach hinges on the observation that the expectation of the data matrix has a low-rank decomposition under a GoM model. For identifiability, we develop sufficient and almost necessary conditions for a notion of expectation identifiability. For estimation, we extract only a few leading singular vectors of the observed data matrix and exploit the simplex geometry of these vectors to estimate the mixed membership scores and other parameters. We also establish the consistency of our estimator in the double-asymptotic regime where both the number of subjects and the number of items grow to infinity. Our spectral method has a huge computational advantage over Bayesian or likelihood-based methods and is scalable to large-scale and high-dimensional data. Extensive simulation studies demonstrate the superior efficiency and accuracy of our method. We also illustrate our method by applying it to a personality test dataset.

Identification of aging fingerprint of base asphalt by ATR-FTIR combined with chemometrics

In this work, attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with chemometric analysis was used to classify and characterise aged asphalt. The full spectra of asphalt samples with 5 types at 6 aging states (30 samples) were determined using ATR-FTIR and the data matrix of the full spectra was analysed chemometrically. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) outcomes demonstrate that the asphalts of five oil sources are divided into three major categories. The samples of asphalt prior to and following aging with comparable oil sources are still grouped into one category. With up to 100% accuracy, one can discriminate between the asphalt samples from these three oil sources using Fisher's discriminant function based on PCA and HCA cluster analysis.This indicates that the aging characteristics of asphalt depend on the oil source, and the "genetic framework" of asphalt will not change with aging.

THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts

In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This study presents the development of an experimental setup operating in transmission mode within the frequency range of 18 to 40 GHz, which was specifically designed for assessing various quality parameters of hazelnuts. The THz measurements were conducted to distinguish between healthy and rotten hazelnut samples. Two different data analysis techniques were employed and compared: a traditional approach based on data matrix manipulation and curve fitting for parameter extrapolation, and the utilization of a Self-Organizing Map (SOM), for which we use a neural network commonly known as the Kohonen neural network, which is recognized for its efficacy in analyzing THz measurement data. The classification of hazelnuts based on their quality was performed using these techniques. The results obtained from the comparative analysis of coding efforts, analysis times, and outcomes shed light on the potential applications of each method. The findings demonstrate that THz spectroscopy is an effective technique for quality assessment in hazelnuts, and this research serves to clarify the suitability of each analysis technique.

Fast MRI Techniques for Abdominal Imaging Simplified: Part 1—Making Sense of It All

Motion is a major limiting factor in abdominal MRI. Scan time is related to MR signal acquisition and k-space filling. K-space is the raw data matrix that stores digitized MR signal, which is Fourier transformed into an image. So rapid signal acquisition, reduced k-space filling, and partial Fourier techniques can reduce scan time. This article aims to provide a simplified explanation of the physics behind various techniques for fast abdominal MRI.