Matrix Manipulation Research Articles

Recent advances in extracellular matrix manipulation for kidney organoid research.

The kidney plays a crucial role in maintaining the body's microenvironment homeostasis. However, current treatment options and therapeutic agents for chronic kidney disease (CKD) are limited. Fortunately, the advent of kidney organoids has introduced a novel in vitro model for studying kidney diseases and drug screening. Despite significant efforts has been leveraged to mimic the spatial-temporal dynamics of fetal renal development in various types of kidney organoids, there is still a discrepancy in cell types and maturity compared to native kidney tissue. The extracellular matrix (ECM) plays a crucial role in regulating cellular signaling, which ultimately affects cell fate decision. As a result, ECM can refine the microenvironment of organoids, promoting their efficient differentiation and maturation. This review examines the existing techniques for culturing kidney organoids, evaluates the strengths and weaknesses of various types of kidney organoids, and assesses the advancements and limitations associated with the utilization of the ECM in kidney organoid culture. Additionally, it presents a discussion on constructing specific physiological and pathological microenvironments using decellularized extracellular matrix during certain developmental stages or disease occurrences, aiding the development of kidney organoids and disease models.

State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

The conventional weighted least square (WLS) method is the most effective technique used in the state estimation of high voltage transmission system. Unfortunately, the application of WLS in radial distribution network encounter difficulties due to the inherent characteristics of these systems, such as the low measurement redundancy and high r/x ratio of the distribution systems. Given the structure of bulky systems that require a bulky number of measurements, the use of artificial neural networks is considered an effective alternative to estimate these values using a lesser number of measurements than conventional techniques. Due to state estimation based on ANN technique, the time-consuming gain matrix manipulation and pseudo measurements required in the conventional WLS method are no longer necessary. The efficiency of deep learning neural networks such as multi-layer perceptron (MLP) and Legendre neural network (LeNN) depends on the position of the measurements and the number of neural networks applied. Determining the applicable number of neural networks to ensure high estimation accuracy plays an important role in the estimation process. This aspect is addressed in this study, where multiple neural networks are used to improve performance compared to a single neural network. The results obtained indicate that determining the applicable number of neural networks depends on several factors such as the position of the measurements and the diversity of the data. The application of LeNN on state estimation of a 69-bus radial distribution network is used as an illustrative example to explain the distinctive feature of the proposed technique.

A Mathematical Review Study on Dynamical Models of Symmetrical Three-Phase Induction Machine in Various Reference Frames

This paper presents dynamic models of a three-phase induction machine in various reference frames widely employed in alternating-current (ac) machine analysis. The main objective is to derive and explain the machine model in relatively basic terms by using the idea of rotating reference frame theory. Many matrix manipulations and complex frame-to-frame transformations performed to obtain an advanced model from primitive dynamical equations are presented in a more compact and easy-to-understand way. Therefore, this paper reviews a detailed, yet simple and understandable mathematical background on the dynamic models of the induction machine. Furthermore, a unified and broadly applicable simulation model is proposed for simulating the dynamic behavior of the machine in any desired reference frame. The simulation model has also a modular and user-friendly structure. For the simulation studies, Matlab/Simulink environment is preferred due to its popularity. A Simulink machine model with several subsystems is explicitly given. The simulation study is realized for a small power induction machine operating under both load and no-load conditions. The variations of three-phase currents, electromagnetic torque, and rotor mechanical speed as well as the rotor flux-linkage components are shown. The key features of each reference frame are discussed, especially through the measured rotor flux-linkage components.

An assessment of the prominence and total engagement metrics for ranking interdependent attributes in DEMATEL and WINGS

A weight vector is assigned to the attributes in multiple-attribute decision-making to show their relative importance. The interdependencies among the attributes often influence this weight vector. The decision-making trial and evaluation laboratory (DEMATEL) and weighted influence non-linear gauge system (WINGS) are among those methods that consider these interdependencies. These methods require matrix manipulation with several metrics to evaluate interdependencies. This study investigates the potential irregularities within the metrics employed by these two methods for weighing criteria. It examines these metrics and analyzes their sensitivity to the direction and the level of influence among attributes. We provide several numerical examples and mathematical analyses to evaluate their consistency by comparing the expected outcomes with the outcomes of the metrics. Although the metrics are expected to assign higher importance to the more influencing criteria, the total engagement/prominence metric is not sensitive to the direction and level of influence among attributes. We conclude these metrics are inconsistent and can not be used reliably as a composite indicator. In contrast, we show that the total impact factor reflects both the direction and the level of influence and is a reliable choice for this purpose.

Generalized Pulse Width Modulation Switch Model for Converters Based on the Multistate Switching Cell in Discontinuous Conduction Mode.

This work introduces a generalized version of the pulse width modulation (PWM) switch model applied in the small-signal modeling of converters based on the multistate switching cell (MSSC) operating in discontinuous conduction mode (DCM). It consists of extending the concept formerly introduced by Vorperian for the representation of multiphase converters in DCM, yielding a circuit-based approach that does not rely on matrix manipulations unlike state-space averaging (SSA). The derived dc and ac models are valid for any number of switching states and any operating region defined in terms of the duty cycle, thus allowing for determining the voltage gain and distinct transfer functions. A thorough discussion of results is presented to demonstrate the applicability of the derived models to represent distinct configurations of the MSSC.

Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-σ method.

Multidimensional credibility: A new approach based on joint distribution function

AbstractIn the traditional multidimensional credibility models developed by Jewell ((1973) Operations Research Center, pp. 73–77.), the estimation of the hypothetical mean vector involves complex matrix manipulations, which can be challenging to implement in practice. Additionally, the estimation of hyperparameters becomes even more difficult in high-dimensional risk variable scenarios. To address these issues, this paper proposes a new multidimensional credibility model based on the conditional joint distribution function for predicting future premiums. First, we develop an estimator of the joint distribution function of a vector of claims using linear combinations of indicator functions based on past observations. By minimizing the integral of the expected quadratic distance function between the proposed estimator and the true joint distribution function, we obtain the optimal linear Bayesian estimator of the joint distribution function. Using the plug-in method, we obtain an explicit formula for the multidimensional credibility estimator of the hypothetical mean vector. In contrast to the traditional multidimensional credibility approach, our newly proposed estimator does not involve a matrix as the credibility factor, but rather a scalar. This scalar is composed of both population information and sample information, and it still maintains the essential property of increasingness with respect to the sample size. Furthermore, the new estimator based on the joint distribution function can be naturally extended and applied to estimate the process covariance matrix and risk premiums under various premium principles. We further illustrate the performance of the new estimator by comparing it with the traditional multidimensional credibility model using bivariate exponential-gamma and multivariate normal distributions. Finally, we present two real examples to demonstrate the findings of our study.

Fast annual daylighting simulation and high dynamic range image processing using NumPy

Annual daylighting and point-in-time glare calculations depend on manipulating large matrices representing annual light levels and image pixels, respectively. Advances in computation speed have allowed modern green building standards like LEED, WELL, and EN 17037 to require annual daylighting calculations at a fine resolution across a building’s floorplate. As parallel ray tracing speeds become faster and cameras increase in resolution, matrix manipulation itself has emerged as a bottleneck to delivering fast simulation results. This paper describes a how vectorized computation methods can be applied to annual daylighting simulation and high dynamic range (HDR) image processing. Vectorization allows a computer to perform simple operations on large amounts of data simultaneously. We use the NumPy library in Python to vectorize multiple steps in the calculation of spatial daylight autonomy and annual renderings for glare as example climate-based daylighting metrics, achieving a speedup of two orders of magnitude. We also show that NumPy can manipulate large HDR images with speedups for individual operations up to six orders of magnitude.

Monitoring internal heat fluxes on Pulsating Heat Pipes using Kalman filter – Numerical and experimental results

The present study proposes an approach to monitor the wall-to-fluid internal heat flux on Pulsating Heat Pipes (PHP), which provides meaningful information about the efficiency and limiting operation conditions of such devices. In this sense, the current work adopts the Kalman Filter (KF) to estimate the heat flux between the working fluid and the internal tube wall along the adiabatic section of a PHP device by solving an inverse heat conduction problem. The KF is a well-known tracking technique, and therefore the current study is attractive due to the possibility of real-time evaluation of internal heat fluxes, a prospect that is extremely difficult to realize by standard whole domain inverse problem techniques. A high-speed and high-resolution infrared camera performs the temperature acquisition on the external wall of the PHP device. Two mathematical heat conduction models are considered: complete and thin-wall models. The complete model determines the temperature and heat flux evolution over time, and the thin-wall model is used to correct the random walk parameter on the KF formulation. Since the time step needed to the KF evolution model is smaller than the measurements acquisition interval, a matrix manipulation is used during the prediction step, reducing the number of mathematical operations. Hence, instead of predicting the states until reaching the measurement times, a proper mathematical manipulation reduces several mathematical operations to a single one. Then, the KF estimation capability is evaluated by considering synthetic data with different noise levels and different heat flux values. Subsequently, real experimental data are employed, and the results are compared with the ones obtained via a standard whole domain technique. The results obtained with the KF presented good visual agreement with the exact solution. Once the KF presents a delayed response, which is a characteristic of some filtering techniques, the errors with synthetic data were calculated offsetting the delay of the estimation in time in order to make a fair comparison, showing a maximum estimation error lower than 36 % considering the highest noise level. The synthetic data considered 20 s of time experiment, and the KF was able to estimate the PHP internal heat flux in less than 12 s, including the smoothing temperature step, showing fast and accurate results. For the real experimental data, the agreement between the KF and a whole domain technique is highly commendable, demonstrating a deviation of 25.5 % between both techniques. Moreover, the KF required significantly less computational time. The proposed methodology for heat flux estimation on PHPs is promising to evaluate their thermal behavior in real-time due to a low CPU time and computational memory required, as well as its simplicity to be implemented, possibly on embarked devices. Such information may therefore provide a thermal indicator of the correct functioning of PHPs in real time applications.

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.

A multi-attribute decision making context for supply chain management using possibility single-valued neutrosophic soft settings

Argued decisions, risk estimation, empirical study, forecasts, apprehension supervision, and productive exposition depend on the degree of possibility. It enables us to assess the degree to which specific results are acceptable and to make more informed decisions by drawing on the information at our disposal and logic. Although some scholars have previously examined hybrid structures resembling fuzzy soft sets with possibility degree settings serving as fuzzy membership grades, the current study introduces an innovative structure that permits more adaptable and comprehensive settings: single-valued neutrosophic grades, to serve as possibility degrees. Thus, this study aims to introduce a new mathematical structure, i.e., possibility single-valued neutrosophic soft set (psv-NSOS), which combines three important theories (i.e., possibility theory, single-valued neutrosophic theory, and soft set theory). The basic notions, and set-theoretic operations, i.e., union and intersection, of psv-NSOS are investigated and manipulated with matrix representations. Considering evaluating suppliers for a real estate construction project as a multi-attribute decision-making issue, an algorithm that utilizes the matrix manipulations of proposed set-theoretic operations is presented. The suggested algorithm?s robustness is confirmed by comparison to evaluate its dependability and adaptability for modeling uncertainties related to the supplier selection problem.

Singular value decomposition and applications in data processing and artificial intelligence

Computing matrices is a crucial and prevalent topic in the field of data processing and artificial intelligence. One of the significant and effective methods for matrix manipulation is Singular Value Decomposition (SVD). Based on matrix computations using SVD, we can perform various complex operations such as dimensionality reduction, hidden information detection, optimization, and many other applications. Singular Value Decomposition is a valuable method in data science, allowing us to decompose a matrix into its fundamental components. Similar to Principal Component Analysis (PCA), SVD helps reduce the dimensionality of data while preserving the most important information. However, SVD can be applied to non-square, non-invertible matrices, and its ability to separate fundamental components enables us to analyze more complex data. SVD has a wide range of applications in practical scenarios.

Improving flexibility and ease of matrix subsetting: The submatrix command

Matrix manipulation in Stata can be a time-consuming and tedious task, especially when it is necessary to subset or rearrange elements from large matrices based on nonconsecutive elements. Compared with Mata, these tasks require more time, more code, and sometimes more complex output. The purpose of this article is to introduce submatrix, a command to manipulate matrix elements using row (and column) names, numbers, and equations.

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance.

Accuracy verification of fast adaptive galerkin finite volume solver for thermal and mechanical load crack propagation in triangular meshes

This study presents the details of the Galerkin finite volume method for triangular unstructured meshes and its application for the fracture analysis of 2D elasticity problems under simultaneous thermal and mechanical loading. The heat diffusion and elasticity equations are discretized using the Galerkin finite volume method (which, due to omitting matrix manipulation calculations, computations are considerably faster than FEM and XFEM Solvers). The maximum tangential stress criterion is presented after briefly describing the interaction integral formulation used to calculate the stress intensity factors in thermo-mechanical problems. The accuracy of the computed results of the present strategy under thermo-mechanical loads is demonstrated using some benchmark test cases. First, a thermal stress analysis of a plate with an inclined central crack under thermal boundary conditions is performed. Second, the development of a crack under mechanical stress boundary conditions is modeled. Third, crack propagation under both thermal and mechanical boundary conditions is simulated. The present modeling strategy's results are compared with those reported in previous numerical works to verify the accuracy. The stress intensity factors and predicted crack trajectories are utilized to assess the accuracy of computed results and investigate the quality of crack simulation by the proposed numerical method.

Immune checkpoint targeting antibodies hold promise for combinatorial cancer therapeutics.

Through improving the immune system's ability to recognize and combat tumor cells as well as its receptivity to changes in the tumor microenvironment, immunotherapy has emerged as a highly successful addition to the treatment of cancer. However, tumor heterogeneity poses a significant challenge in cancer therapy as it can undermine the anti-tumor immune response through the manipulation of the extracellular matrix. To address these challenges and improve targeted therapies and combination treatments, the food and drug administration has approved several immunomodulatory antibodies to suppress immunological checkpoints. Combinatorial therapies necessitate the identification of multiple targets that regulate the intricate communication between immune cells, cytokines, chemokines, and cellular responses within the tumor microenvironment. The purpose of this study is to provide a comprehensive overview of the ongoing clinical trials involving immunomodulatory antibodies in various cancer types. It explores the potential of these antibodies to modulate the immune system and enhance anti-tumor responses. Additionally, it discusses the perspectives and prospects of immunomodulatory therapeutics in cancer treatment. Although immunotherapy shows great promise in cancer treatment, it is not exempt from side effects that can arise due to hyperactivity of the immune system. Therefore, understanding the intricate balance between immune activation and regulation is crucial for minimizing these adverse effects and optimizing treatment outcomes. This study aims to contribute to the growing body of knowledge surrounding immunomodulatory antibodies and their potential as effective therapeutic options in cancer treatment, ultimately paving the way for improved patient outcomes and deepening our perception of the intricate interactivity between the immune system and tumors.

A Fuzzy Parameterized Multiattribute Decision-Making Framework for Supplier Chain Management Based on Picture Fuzzy Soft Information

Supplier selection as a multiattribute decision-making (MADM) problem has various inherent uncertainties due to a number of symmetrical variables. In order to handle such information-based uncertainties, rational models like intuitionistic fuzzy sets have already been introduced in the literature. However, a picture fuzzy set (PiFS) with four dimensions of positive, neutral, negative, and rejection is better at capturing and interpreting such kinds of ambiguous information. Additionally, fuzzy parameterization (FPara) is helpful for evaluating the degree of uncertainty in the parameters. This study aims to develop a fuzzy parameterized picture fuzzy soft set (FpPiFSS) by integrating the ideas of PiFS and FPara. This integration is more adaptable and practical since it helps decision makers manage approximation depending on their objectivity and parameterization uncertainty. With the assistance of instructive examples, some of the set-theoretic operations are examined. A decision support framework is constructed using matrix manipulation, preferential weighting, fuzzy parameterized grades based on Pythagorean means, and the approximations of decision makers. This framework proposes a reliable algorithm to evaluate four timber suppliers (initially scrutinized by perusal process) based on eight categorical parameters for real estate projects. In order to accomplish suppliers evaluation, crucial validation outcomes are taken into account, including delivery level, purchase cost, capacity, product quality, lead time, green degree, location, and flexibility. To assess the advantages, dependability, and flexibility of the recommended strategy, comparisons in terms of computation and structure are provided. Consequently, the results are found to be reliable, analog, and consistent despite the use of fuzzy parameterization and picture fuzzy setting.

Modeling a loudspeaker system with a flexible enclosure using finite element and lumped parameter model

A modeling method combining finite element and lumped parameter (FE-LP) model is proposed for accurately simulating loudspeaker systems with flexible enclosures, such as speakerphones, AI speakers, and portable Bluetooth speakers. In this approach, the mechanical components of the driver unit, except for the cone assembly (consisting of the dust cap, cone, and surround), are represented by lumped parameters, while the cone assembly and enclosure are modeled using the finite element method. The coupled equations of motion for the cone assembly and enclosure are derived to describe their dynamic behavior accurately. The coupling between the lumped parameters and finite elements is achieved through the manipulation of the system matrix. The validity of the proposed FE-LP method is demonstrated through numerical examples, including tests for the case of a loudspeaker system with a rigid enclosure. The results of these tests confirm that the proposed method can be applied to loudspeaker systems with enclosures of any rigidity.

Graphical reduction and analysis small-angle neutron scattering program: GRASP.

GRASP is a scientific software application designed for the graphical inspection, reduction and analysis of multidetector data produced by the small-angle neutron scattering (SANS) instruments at the Institut Laue-Langevin and other neutron sources around the world. The first developments of GRASP began more than 20 years ago and were written in MATLAB, allowing rapid development of scientific code, with much of the data handling, matrix manipulation, mathematical tools, user interface and graphical tools integrated at a high level in the underlying MATLAB platform. By their very nature, multidimensional data are often best appreciated in graphical form. GRASP deals with many of the diverse requirements for data reduction and analysis of SANS data using a general set of tools and reduction algorithms suited to 2D multidetector data. A further fundamental architectural inclusion is a third dimension of data manipulation, thereby easily allowing parametric analysis and cross referencing of series data such as composition, kinetic measurements, temperature, magnetic field, angle or time of flight, often considered as a single 'measurement'. This article serves as a reference document for users of the software, and outlines the architecture and strategy of the program. An overview of some of the features, capabilities, peripheral user modules and neutron scattering tools is presented.

Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms.

Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50-60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques.