Loading Matrix Research Articles

Modeling the effect of initial cell concentration and soluble solids on the plasma inactivation of yeast in apple juices

Atmospheric cold plasma is a powerful and profitable microbial decontamination technology in the food industry. The microbial load and food matrix have a great influence on the plasma inactivation effect. This study aimed to quantify the effects of initial cell concentration and soluble solids on the inactivation of yeast in apple juice by gas-phase surface discharge plasma. A computer simulation model was developed to predict the inactivation process using commercial finite element software, COMSOL Multiphysics, and the model was verified by experiments. Studies have shown that the inactivation of yeast was affected by the initial cell concentration and soluble solids. These effects were quantified by fitting equations and used as model inputs. The simulation model predicted the D-value of plasma inactivation of yeast under different discharge voltages and apple juice volume, which was verified in experiments. Therefore, the established model can be used for plasma development to predict how the food matrix affects the inactivation process, thereby providing useful and practical tools for amplifying the commercial application of plasma.

Modeling parameters influencing city gas distribution sector based on factor analysis method

City gas distribution (CGD) sector is influenced by several factors like policy, infrastructure, health, safety etc. In order to understand this sector, an exploratory factor analysis is conducted. The Exploratory Factor Analysis (EFA) survey meticulously simplifies interconnected steps and examines the possible causal factor structure of a series of measured variables without hitting a predetermined result model. In this paper, the factor analysis is performed in three broad categories namely: managerial level, technical level and site workers to understand the most influencing factor of the sector. 60 questionnaires were prepared to get feedback on parameters affecting CGD sector. The survey is performed by various means, like google form, email, phone calls, appointments with employees and personal meetings. It has been observed from the survey that the nine factors influences this sector and requires certain modifications for the development. Out of these nine factors, five were selected for the analysis which are infrastructure factor, policy factor, gas consumption factor, total energy demand factor and economy factor. The factor analysis has been performed in five major steps, namely factor analysis applicability, selection of factors, loading of factors, significance test of factors and factor loading matrix analysis. The results obtained from these exploratory factor analysis shows that variables like infrastructure, total energy demand and economy affects the CGD market most than the policy and gas consumption.

Constrained principal component analysis with stochastically ordered scores for high-dimensional mass spectrometry data

In this paper, we consider a constrained principal component analysis (PCA) for the projection of high-dimensional samples from different groups to a lower-dimensional space for which the principal scores are stochastically ordered over the groups. We express the problem as the minimization of a constrained biconvex problem and develop an iterative algorithm to solve it. We numerically show that the solution to our constrained PCA problem approximately rotates the principal coordinates of the ordinary PCA to achieve ordered scores. Consequently, our approach significantly improves the scores and the corresponding loading matrix compared to the original PCA if their true values are ordered over groups. We finally apply our method to two data examples: (i) the direct infusion multiple reaction monitoring mass spectrometry (DI-MRM-MS) data of white rice to verify the authenticity of adulterated Japonica rice and (ii) high-dimensional lipidomics data from ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS) analysis of patients with liver diseases.

Task-adaptive eigenvector-based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor.

The compressed sensing (CS) theory requires the signal to be sparse under some transform. For most signals (e.g., speech and photos), the non-adaptive transform bases, such as discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh-Hadamard transform (WHT), can meet this requirement and perform quite well. However, one limitation of these non-adaptive transforms is that we cannot leverage domain-specific knowledge to improve CS efficiency. This study presents a task-adaptive eigenvector-based projection (EBP) transform. The EBP basis has an equivalent effect of the principal component loading matrix and can generate a sparse representation in the latent space. In a Raman spectroscopic profiling case study, EBP demonstrates better performance than its non-adaptive counterparts. At the 1% CS sampling ratio (k), the reconstruction relative mean square errors of DCT, DFT, WHT and EBP are 0.33, 0.68, 0.32, and 0.00, respectively. At a fixed k, EBP achieves much better reconstruction quality than the non-adaptive counterparts. For specific domain tasks, EBP can significantly lower the CS sampling ratio and reduce the overall measurement cost.

Sparse principal component analysis using bootstrap method

Sparse principal component analysis (SPCA) gives a sparse description of the loading matrix. In order to determine the “0” values in the loading matrix, often artificial thresholds are set on the loading values. To resolve these issues, two methods are proposed in this article to calculate the confidence intervals of the loading values. The first method is based on a resampling technique, while the second method estimates the error variance of data to calculate confidence intervals of the loadings. The position and number of non-zero loadings (NNZL) for the PCs are chosen based on a hypothesis test for “0”. Both methods lead to sparse structures of PCs. The fault detection and diagnosis performance of the proposed SPCA techniques are compared with the traditional PCA and three widely used SPCA methods for the benchmark continuous stirred tank heater (CSTH) process. The outcomes indicate that the proposed approaches perform better than the traditional PCA and benchmark SPCA (i.e., AV) method in fault detection and diagnosis.

Generalized mixed spatio-temporal modeling: Random effect via factor analysis with nonlinear interaction for cluster detection

In this study, we develop factor analysis to explore areal data collected in space and time. The main goal is to incorporate the framework with nonlinear interactions to handle a spatio-temporal random effect in the structure of a mixed generalized linear regression. The spatial dependence between regions is established through the CAR model specified for each column of the loadings matrix. Temporal dependence is considered to associate the columns of the factor scores matrix. The presence of nonlinear interactions is intended to improve cluster detection, since new types of groups can emerge as a combination of the main factors effects and the interaction effect. Our study is focused on the logistic and Poisson cases, but it can be extended to other generalized linear models that originated from distributions of the exponential family. A comprehensive simulation study is conducted to investigate the performance of the proposed approach. This work was motivated by the analysis of electrocardiogram (ECG) data related to patients affected by acute myocardial infarction (AMI). The data were collected between 2013 and 2016 through an ECG telediagnostic system covering the state of Minas Gerais in Brazil. The system is maintained by the Telehealth Center within the Hospital das Clínicas of the Federal University of Minas Gerais. The methodology proposed defining nonlinear interaction in the spatio-temporal setting and the analysis of the novel ECG data set are the central contributions of the paper.

Relaxation capacity of cartilage is a critical factor in rate- and integrity-dependent fracture

Articular cartilage heals poorly but experiences mechanically induced damage across a broad range of loading rates and matrix integrity. Because loading rates and matrix integrity affect cartilage mechanical responses due to poroviscoelastic relaxation mechanisms, their effects on cartilage failure are important for assessing and preventing failure. This paper investigated rate- and integrity-dependent crack nucleation in cartilage from pre- to post-relaxation timescales. Rate-dependent crack nucleation and relaxation responses were obtained as a function of matrix integrity through microindentation. Total work for crack nucleation increased with decreased matrix integrity, and with decreased loading rates. Critical energy release rate of intact cartilage was estimated as 2.39 ± 1.39 to 2.48 ± 1.26 kJ m−2 in a pre-relaxation timescale. These findings showed that crack nucleation is delayed when cartilage can accommodate localized loading through poroviscoelastic relaxation mechanisms before fracture at a given loading rate and integrity state.

Time-Varying Comovement of Foreign Exchange Markets: A GLS-Based Time-Varying Model Approach

How strongly are foreign exchange markets linked in terms of their similarities in long-run fluctuations? Are they cointegrating? To analyze such “comovements,” we present a time-varying cointegration model for the foreign exchange rates of the currencies of Canada, Japan, and the UK vis-à-vis the U.S. dollar from May 1990 through July 2015. Unlike previous studies, we allow the loading matrix in the vector error-correction (VEC) model to be varying over time. Because the loading matrix in the VEC model is associated with the speed at which deviations from the long-run relationship disappear, we propose a new degree of market comovement based on the time-varying loading matrix to measure the strength or robustness of the long-run relationship over time. Since exchange rates are determined by macrovariables, cointegration among exchange rates implies these variables share common stochastic trends. Therefore, the proposed degree measures the degree of market comovement. Our main finding is that the market comovement has become stronger over the past quarter-century, but at a decreasing rate with two major turning points: one in 1995 and the other one in 2008.

A Simplified Analysis Method for the Deformation Response of an Existing Tunnel to Ground Surcharge Based on the Pasternak Model

Surface surcharge changes the existing equilibrium stress field of the stratum and adversely affects the existing tunnel. This paper presents a simplified analytical solution for calculating the longitudinal displacement of existing tunnels that are subjected to adjacent surcharge loading. Based on the Boussinesq solution, the distribution of the additional load matrix caused by the surface surcharge on the existing tunnel was obtained. A Euler–Bernoulli beam with a Pasternak foundation was used as a simplified model for tunnel stress analysis. Using the corrected reaction coefficient of the foundation bed, the differential equation of tunnel deformation was established, and the solution matrix of the longitudinal displacement of the tunnel was obtained by using the finite difference method. The reliability and applicability of the proposed method were verified by comparing the results with finite element simulation results, field test data, and the calculation results of three simplified elastic analysis methods with different foundation bed coefficients. On this basis, the parameters of the load–tunnel model were analyzed, and the effects of the buried depth, the size of the load, the relative positions of the load and the tunnel, and the relative stiffness of the tunnel soil on the maximum displacement of the existing tunnel were calculated. An empirical formula is proposed for calculating the maximum longitudinal displacement of the existing tunnel subjected to surface surcharge. The findings of this research can provide a basis for the theoretical verification of the deformation response of an existing tunnel subjected to adjacent surface surcharge.

Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data

We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic macroeconomic factor, we can measure to what extent the different types of education exhibit similarities in their relationship with macroeconomic cycles. To utilize the feature that unemployment data is available for a longer time period than our detailed education panel data, we propose a two-step procedure. First, we consider a score-driven model which filters the conditional expectation of the unemployment rate. Second, we consider a multivariate model in which we regress the number of students on the dynamic macroeconomic factor, and we further apply the k-means method to estimate the clustered loading matrix. In a Monte Carlo study, we analyze the performance of the proposed procedure in its ability to accurately capture clusters and preserve or enhance forecasting accuracy. For a high-dimensional, nation-wide data set from the Netherlands, we empirically investigate the impact of the rate of unemployment on choices in education over time. Our analysis confirms that the number of students in part-time education covaries more strongly with unemployment than those in full-time education.

Identification of key performance indicators and complimentary load profiles for 5th generation district energy networks

Mass adoption of renewable heating is essential for achieving Net Zero 2050 emission targets. Rapid decarbonisation of heating could be delivered by 5th generation district heating networks, which share heating and cooling and offer energy, cost and carbon savings. We present an assessment framework for determining the economic, operational, and carbon benefits of heat pump driven energy sharing networks for an urban centre. Our analysis of complementary heating and cooling loads enabled novel identification of the building types which are best suited to thermal energy sharing. An urban street was modelled using Integrated Energy System Virtual Environment software, which produced heating, cooling, and hot water loads. These were implemented into a linear programming cost and carbon optimisation problem, producing operating curves for a pool of de-localised heat pumps under either cost or emission minimalization scenarios. Results show that energy sharing networks may reduce the Levelised Cost of Energy by 69% and carbon emissions by 13% when compared to an electrified non-shared energy system. Based on these findings, a load matrix was constructed to identify which energy loads from different building types can be suitably used for energy sharing. Despite promising cost-savings results, we conclude that low temperature district heating networks have much greater financial benefit when utilising appropriately sized thermal storage and time of use tariffs, rather than energy sharing. However, carbon savings can be made over alternatives, such as natural gas boilers. For developers undertaking a heat network project, the primary goal should be clearly defined as either carbon saving or money-making objective, as these are difficult to achieve synergistically.

Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection

Penalized factor analysis is an efficient technique that produces a factor loading matrix with many zero elements thanks to the introduction of sparsity-inducing penalties within the estimation process. However, sparse solutions and stable model selection procedures are only possible if the employed penalty is non-differentiable, which poses certain theoretical and computational challenges. This article proposes a general penalized likelihood-based estimation approach for single- and multiple-group factor analysis models. The framework builds upon differentiable approximations of non-differentiable penalties, a theoretically founded definition of degrees of freedom, and an algorithm with integrated automatic multiple tuning parameter selection that exploits second-order analytical derivative information. The proposed approach is evaluated in two simulation studies and illustrated using a real data set. All the necessary routines are integrated into the R package penfa.

Poisson reduced-rank models with sparse loadings

High-dimensional Poisson reduced-rank models have been considered for statistical inference on low-dimensional locations of the individuals based on the observations of high-dimensional count vectors. In this study, we assume sparsity on a so-called loading matrix to enhance its interpretability. The sparsity assumption leads to the use of $$L_1$$ penalty, for the estimation of the loading. We provide novel computational and theoretical analyses for the corresponding penalized Poisson maximum likelihood estimation. We establish theoretical convergence rates for the parameters under weak-dependence conditions; this implies consistency even in large-dimensional problems. To implement the proposed method involving several computational issues, including nonconvex log-likelihoods, $$L_1$$ penalty, and orthogonal constraints, we developed an iterative algorithm. Further, we propose a Bayesian-Information-Criteria-based penalty parameter selection, which works well in the implementation. Some numerical evidence is provided by conducting real-data-based simulation analyses and the proposed method is illustrated with the analysis of German party manifesto data.

Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions

Damage detection of civil engineering structures needs to consider the effect of normal environmental variations on structural dynamic properties. This study develops a novel structural damage detection method using factor analysis in the sparse Bayesian learning framework. The unknown changing environmental factors that affect the structural dynamic properties are treated as latent variables in the model. The automatic relevance determination prior is adopted for the factor loading matrix for model selection. All variables and parameters, including the factor loading matrix, error vector and latent variables, are solved using the iterative expectation-maximization technique. The variables are then used to reconstruct structural responses. The Euclidean norm of the error vector is calculated as the damage indicator to detect possible damage when limited vibration data are available. Two laboratory-tested examples are utilized to verify the effectiveness of the proposed method. Results demonstrate that the number of underlying environmental factors and structural damage can be accurately identified, even though the changing environmental data are unavailable. The proposed method has the advantages of online monitoring and automatic identification of underlying environmental factors.

Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces

We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on factor loadings. For each column of the loadings matrix, spatial dependency is encoded using a probit stick-breaking process (PSBP) and a multiplicative gamma process shrinkage prior is used across columns to adaptively determine the number of latent factors. By encoding spatial information into the loadings matrix, meaningful factors are learned that respect the observed neighborhood dependencies, making them useful for assessing rates over space. Furthermore, the spatial PSBP prior can be used for clustering temporal trends, allowing users to identify regions within the spatial domain with similar temporal trajectories, an important task in many applied settings. In the manuscript, we illustrate the model’s performance in simulated data, but also in two real-world examples: longitudinal monitoring of glaucoma and malaria surveillance across the Peruvian Amazon. The R package spBFA, available on CRAN, implements the method.

On the equivalency of factor and network loadings.

Recent research has demonstrated that the network measure node strength or sum of a node's connections is roughly equivalent to confirmatory factor analysis (CFA) loadings. A key finding of this research is that node strength represents a combination of different latent causes. In the present research, we sought to circumvent this issue by formulating a network equivalent of factor loadings, which we call network loadings. In two simulations, we evaluated whether these network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the simulated factor loading matrix of factor models. Our findings suggest that the network loadings can effectively do both. In addition, we leveraged the second simulation to derive effect size guidelines for network loadings. In a third simulation, we evaluated the similarities and differences between factor and network loadings when the data were generated from random, factor, and network models. We found sufficient differences between the loadings, which allowed us to develop an algorithm to predict the data generating model called the Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the data generating model. In sum, our results suggest that network loadings can provide similar information to factor loadings when the data are generated from a factor model and therefore can be used in a similar way (e.g., item selection, measurement invariance, factor scores).

Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never been explored using information modelling techniques. Further, promulgation of contemporary research trends in Android malware research has never been done from semantic point of view. This paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis (LSA). An extensive corpus of 843 articles on Android malware and security, published during 2009–2019, were processed using LSA. Subsequently, the truncated singular Value Decomposition (SVD) technique was used for dimensionality reduction. Later, machine learning methods were deployed to effectively segregate prominent topic solutions with minimal bias. Apropos to observed term and document loading matrix values, this five core research areas and twenty research trends were identified. Further, potential future research directions have been detailed to offer a quick reference for information scientists. The study concludes to the fact that Android security is crucial for pervasive Android devices. Static analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near future. Research trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation, financial attacks and stealing user information.

Association and Principal Component Analyses of Eating Quality Traits of 141 <i>Japonica</i> Rice Cultivars in China

Eating quality is the important trait of rice, and rice breeders also employ eating quality in breeding selection for advanced generations. Therefore, to better understand the factors affecting the eating quality of japonica rice in China, the physicochemical indices [apparent amylose content (AAC), gel consistency (GC), protein content (PC), and gelatinization temperature (GT)], rapid visco analyzer parameters, and cooked rice taste values of 141 rice cultivars were analyzed. The rice cultivars were divided into low, medium, and total AAC groups. Association and principal component analyses of these indices were performed. In total AAC rice, cooked rice taste value showed a significant negative correlation with AAC, final viscosity (CPV), setback viscosity (SBV), consistence viscosity (CSV), and a positive correlation with GC, peak viscosity (PKV), holding strength (HPV), and breakdown viscosity (BDV). PC showed a significant negative correlation with cooked rice taste value in low, medium, and total AAC rice. For both low and medium AAC rice, the factor load matrix of PC and cooked rice taste value in factor 1 was remarkably higher than that of the other indices, indicating that PC was the most important factor for the eating quality of low and medium AAC rice. For total AAC rice, the factor load matrix of AAC, GC, and cooked rice taste value in factor 1 was remarkably higher than that of the other indices, indicating that AAC and GC were important factors for the eating quality of different type rice cultivars. The results showed that AAC and GC were more important for the eating quality of different type rice cultivars, while PC was more important for the eating quality of similar type rice cultivars. Our findings offer new insight into target traits in breeding rice with high eating quality.

ДОСЛІДЖЕННЯ ТА МОДЕЛЮВАНЯ БЕЗРОБІТТЯ В УКРАЇНІ: КОРЕЛЯЦІЙНО-РЕГРЕСІЙНИЙ АНАЛІЗ

The aim of the study is to conduct a statistical analysis of the modern labor market and adapt a multivariate econometric model of unemployment in Ukraine using the principal component analysis. The paper investigated the current state of unemployment in Ukraine for the last two decades. The dynamics of the unemployment rate and employment of the economically active population in Ukraine is analyzed. The analysis of the structure of the unemployed for reasons of dismissal and the tendency of changes in the size of the working age population is carried out. The gender aspect of the number of the unemployed population is investigated. Comprehensive assessments of the resources of labor potential by regions have been calculated and a rating of regions has been built. The disproportionality behind the Harrington`s desirability function was analyzed taking into account the factors of stimulants and de-stimulants. The rating assessment of unemployment for regional labor markets of Ukraine is given for the gradation of values of the desirability function. The main macroeconomic factors of influence on the level of unemployment in Ukraine have been determined. Structural and correlation-regression relationships have been analyzed. The identification of the model has been carried out. The multivariate unemployment model was adapted. The factorial database was checked for the presence of multicollinearity behind the Ferrer - Glober algorithm based on the criteria: Fisher, Spearman and Student. With the help of component analysis, the study of the relationship between factor variables was carried out. The factor loading matrix was constructed and analyzed. The matrix of the values of the principal components was calculated. The model of unemployment is constructed by the principal component analysis. The model was tested for adequacy, its economic content was analyzed. The residuals (random variables) are estimated to establish the quality of the constructed multivariate model. Dynamic models of factorial variables were built and their values for the next year were estimated. Through the normalized values of estimates of factor variables for dynamic models, the unemployment rate in Ukraine for the future (2021) was calculated.

Study on the effect of habitat function change on waterbird diversity and guilds in Yancheng coastal wetlands based on structure–function coupling

In recent years, due to human activities and global climate change, the number and area of waterbird habitats in Yancheng coastal wetlands, a newly established world natural heritage site, have been shrinking, the function of the waterbird habitats has been degraded to varying degrees, and the safety of waterbird populations and habitats has been threatened. Therefore, in this study, waterbird habitat functions at the regional scale were assessed by using spatial principal component analysis (SPCA). According to the load matrix of principal components and the Redundancy analysis (RDA) method, the driving force of habitat function change and the mechanisms of their effects on waterbird guilds and diversity were revealed. The results showed that the areas of the highest and high habitat function grades continued to decline from 1987 to 2019, and the areas of the lowest and low grades of habitat function continued to increase. The overall habitat functions in the waterbird distribution areas were high, but the functional grades within different habitat sites were obviously decreasing. The main driving factors affecting habitat function change in Yancheng coastal wetlands from 1987 to 2019 were habitat types, habitat area, habitat diversity, habitat fragmentation, number of residential areas and reclamation intensity. The effects of these driving factors on the population diversity and distribution of waterbird guilds were different. Finally, countermeasures and suggestions for the functional maintenance and key node restoration of waterbird habitats in Yancheng coastal wetlands were proposed.