Principal Square Research Articles

Wind loads on a square high-rise building under aerodynamic interference effects of a short building in close proximity

Although the interference effect of an equally high or higher building has been examined extensively, the interference effect of a short building in close proximity on the wind pressure of a high-rise building has not yet been investigated comprehensively and thoroughly. In view of the fact that a newly built wind-sensitive high-rise building is more likely to be close to neighboring shorter buildings, this research focuses on wind loads of a square high-rise building under the interference effect of a short building in close proximity. Wind pressure on the principal square building model is measured by wind tunnel test for a series of cases of interfering buildings with different heights and gap distances. In addition, computational fluid dynamics simulation is conducted for a few typical cases. Characteristics of wind flow and pressure on the principal building are then discussed in detail for three typical interfering configurations to uncover the mechanism of interference effect of a short building. Although a short interfering building in many cases tends to decrease wind loads on the high-rise building, especially for the dynamic component of wind loads, the interfering effect from a short building could induce damage to cladding structures from the increased tension caused by the asymmetrical wind pressure, channeling effect, and the wind-over-top flow over the short interfering building.

Relativistic Free Schrödinger Equation for Massive Particles in Schwartz Distribution Spaces

In this work, we pose and solve, in tempered distribution spaces, an open problem proposed by Schrödinger in 1925. In particular, on the Schwartz distribution spaces, we define the linear continuous quantum operators associated with relativistic Hamiltonians of massive particles—particles with rest mass different from 0 and evolving in the four-dimensional Minkowski vector space M4. In other words, upon the tempered distribution state-space S′(M4,C), we have found the most natural way to introduce the free-particle relativistic Hamiltonian operator and its corresponding Schrödinger equation (together with its conjugate equation, standing for antiparticles). We have found the entire solution space of our relativistic linear continuous evolution equation by completely solving a division problem in tempered distribution space. We define the Hamiltonian (Schwartz diagonalizable) operator as the principal square root of a strictly positive, Schwartz diagonalizable second-order differential operator (linked with the “Klein–Gordon operator” on the tempered distribution space S4′). The principal square root of a Schwartz nondefective operator is defined in a straightforward way—following the heuristic fashion of some classic and greatly efficient quantum theoretical approach—in the paper itself.

Large eddy simulation on wind-induced interference effects of staggered chamfered square cylinders

Chamfered corners in buildings are the main means to reduce the control effect of wind load on the structure, and the interference effect of chamfered buildings cannot be ignored. At present, only the mutual interference coefficients of square and rectangular section buildings are given in the Chinese code, without the interference effect of chamfered buildings being specified. Therefore, in this paper, aerodynamic force and wind pressure coefficients of chamfered square cylinders of different spacing are obtained by the large eddy simulation method. Wind load characteristics, non-Gaussian characteristics and interference effects of chamfered square cylinders with different arrangements are studied based on aerodynamic coefficients, wind pressure coefficients and interference coefficients. The results show that when the wall y plus value is 1, the large eddy simulation is the most accurate to simulate the wind load and wind field parameters. Besides, the aerodynamic effects, non-Gaussian characteristics and interference effects between the chamfered square cylinders are mainly controlled by the cross-wind interval and the spacing (4.0, 4.0) is the characteristic coordinate. That means, when the spacing is smaller than this coordinate, the interference effect of the square cylinder is more obvious. When the spacing coordinate is greater than (4.0, 4.0), the aerodynamic coefficients and non-Gaussian regional distributions of the principal square cylinder and the isolated cylinder are the same, and the interference factor approaches 1.

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018.

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial–temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014–2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

Second order microstrip bandpass filter design based on square resonator for 5G sub-6 GHz band

A simple procedure for designing a highly selective microstrip bandpass filter of second order is presented in this paper. The proposed filter is composed of two principal square open-loop resonators and designed with planar structures on a Roger substrate. It is intended for 5G applications, especially for Sub-6 GHz range frequency [3.5–3.8 GHz]. The suggested band pass filter has a very attractive compact size of 7 mm × 15.46 mm. The performance of this simple miniaturized filter, which is characterized by high selectivity in the band of interest, can be seen from its important characteristics: Adaptation, Transmission and Coupling Coefficients. The electromagnetic simulations and measurement results are in good agreement, which validates the design idea and procedure.

Liquid Chromatographic Fingerprints for the Characterization of Flavanol-Rich Nutraceuticals Based on 4-Dimethylaminocinnamaldehyde Precolumn Derivatization

Flavanols consist of a great family of bioactive molecules displaying a wide range of health-promoting attributes for humans, including antioxidant, antimicrobial or anti-inflammatory effects. As a result, botanical species rich in this type of compound are often used to develop nutraceutical products or dietary supplements with recognized healthy attributes. This paper aims at characterizing nutraceutical products using liquid chromatographic fingerprints related to flavanol composition. Catechins and their oligomers were exploited to characterize and authenticate various commercial products prepared with extracts of red berries and medicinal plants. These compounds resulted in interesting descriptors of some fruits and vegetables, thus providing an additional perspective for the study of nutraceuticals. For such a purpose, a new method based on liquid chromatography with UV/Vis detection (HPLC–UV/Vis) with precolumn derivatization with 4-dimethylaminocinnamaldehyde was developed. Results indicated that the separation of flavanols was very complex due to the degradation of procyanidin derivatives. The resulting data sets were analyzed using chemometric methods such as principal component analysis and partial least square–discriminant analysis. Despite the complexity of chromatographic fingerprints, nutraceutical samples could be discriminated according to their main ingredients. In general, catechin and epicatechin were the most abundant compounds in the different samples, and procyanidin A2 was highly specific to cranberry.

Appraisal of Three Proximal Sensing Systems to Estimate Macronutrient Contents of Detached Soybean Leaves

ABSTRACT Overapplication of fertilizers is common and may lead to plant toxicity and pollution of water resources in agriculture. A practical method is needed to estimate nutrient stress quickly, easily, and economically for a sustainable management. This study dealt with the prediction of leaf moisture content (MC) and macronutrient contents including nitrogen (N), phosphorus (P), and potassium (K) of soybean leaves using three different optical instruments of chromameter, chlorophyll meter and fluorometer. Forty-five leaf samples from a commercial soybean field were obtained. Color parameters (L*, a*, b*), SPAD (soil plant analysis development) values and quantum yield (QY) values were quantified for each leaf sample. Leaf and soil samples were analyzed using standard leaf and soil chemical analysis procedures. Correlation analysis, principal component analysis (PCA) and partial least square regression (PLSR) were used for data analysis. High correlation was found between two color parameters (L* and b*) with MC and macronutrient contents (r ≥ 0.65) and also, among SPAD, QY, MC, and macronutrient contents (r ≥ 0.70). The prediction models were evaluated based on the RMSEP (root mean square error of prediction) and R 2 (coefficient of determination) values. The results showed that soybean leaf N content can be estimated using all three optical instruments with chromameter giving slightly better results (RMSEP = 0.23%, R 2 = 0.80). Concerning the MC, chlorophyll meter gave slightly better prediction performance (RMSEP = 2.69%, R 2 = 0.73) than the other two instruments. Hence, results suggest that these three optical instruments can be used to assess the macronutrient contents of soybean leaves quickly, easily, and economically.

KPLS–KSER based approach for quality-related monitoring of nonlinear process

As a commonly used data modeling method, kernel principal least square (KPLS) has been widely used in a variety of process monitoring tasks like soft sensing and fault detection. However, it is not easy to implement fault diagnosis based on KPLS model because the nonlinear mapping of kernel methodology completely obscures the correspondence between the original variable sample and the kernel model. Although the kernel-gradient based algorithms have successfully extracted the analytical solution of contribution plot, they are still very inefficient and impractical because of their high amount of computation. In order to overcome this inherent defect, a new idea of kernel sample equivalent replacement (KSER) will be used in this paper, which reveals an important relationship between centralized kernel matrix and the variance–covariance matrix of centralized variable matrix. By integrating KSER into KPLS, a linear regression between the original variable sample and the output could be directly constructed. This approach makes it possible to use the linear algorithm to diagnose the faulty variables. Based on which, an improved contribution plot algorithm is further integrated to the new model to locate faulty variables. Thereby, the complex nonlinear diagnosis problem can be finally solved by a linear manner with extremely low computational complexity. Simulation results of both numerical and industrial examples demonstrate the effectiveness of the proposed method.

Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

Measuring and Modelling the Adaptive Capacity of Universities: A Composite Indicators Methodology

Initiating, implementing, and sustaining reforms are the most daunting and challenging aspects of change in higher education institutions. This research argues that to survive and thrive in a changing environment, adaptive capacity is fundamental. Thus, this study aims to serve two purposes. While the leading purpose was measuring the adaptive capacity of universities using a conceptual model developed based on literature review, introducing the critical components that determine adaptive capacity was secondary. Therefore, guided by a pragmatic approach this study has tried to maintain both subjective reflections and objective data during data collection and analysis. Accordingly, to attain the purpose of the study mixed research design particularly, a sequential explanatory approach was used. To gather relevant data from the target population, the questionnaire was distributed and 594 completed and useful questionnaires were returned from 133 leaders at different position, 268 academic staffs and 193 support staffs, which was a 62.06% response rate. Besides, 15 interviews and documents were reviewed. Finally, among the composite indicators approach, structural equation modeling (SEM) mainly principal least square path modeling (PLS-PM) was used to measure and analyze adaptive capacity using XLSTAT software. As a result, the adaptive capacity of universities was found unsatisfactory i.e. lower than moderate. This indicates universities might not fully implement the change ideas they wish to introduce into their system. Consequently, the identified gaps and challenges in the studied universities strengthen the argument for the need to assess universities adaptive capacity using a model and make appropriate intervention before struggling to introduce any change initiative without a fertile ground.

Entre os trilhos, a estação e as memórias: o papel da estrada de ferro para o desenvolvimento da cidade de Cedro-CE

As ferrovias tiveram uma grande importância no processo de expansão do capitalismo. No território cearense não ocorreu de forma diferente, as ferrovias tinham como atribuições adentrar ao interior do estado e estabelecer vários entrepostos comerciais, assegurando um fluxo constante de mercadorias, produtos agrícolas, pessoas e informação ao longo dos trilhos, interligando os mercados consumidores do litoral cearense com as principais praças comerciais e zonas produtoras do interior do Ceará e estados vizinhos. Para melhor compreender a influência da Rede Viação Cearense para a organização espacial das pequenas cidades às margens dos seus trilhos, resolvemos realizar um estudo histórico sobre a influência da ferrovia para a organização socioespacial da cidade de Cedro-CE. Nessa perspectiva analisaremos a atuação da ferrovia nos processos históricos, socioespaciais e econômicos que levaram a formação e desenvolvimento do município, e como isso ficou registrado no atual espaço urbano, influenciando o cotidiano da cidade.

Analysis of Missile Longitudinal Autopilot Based on the State-Dependent Riccati Equation Method

An analytic solution of the state-dependent Riccati equation (SDRE) method is presented for two-dimensional nonlinear systems, and the closed-loop system of the missile longitudinal autopilot designed by the SDRE method is analyzed using the analytic solution. The analytic solution is obtained using the matrix sign function of the Hamiltonian matrix corresponding to the SDRE, where the matrix sign function is represented using the principal square root of the Hamiltonian matrix. The missile longitudinal acceleration autopilot is designed based on the output feedback SDRE method for command tracking. The asymptotic stability of the closed-loop system and the nonsingularity of the output feedback control command are shown using the analytic solution to the SDRE method. A numerical example of the main results is provided, including the effects of aerodynamic uncertainty. Additionally, a six-degrees-of-freedom simulation for the skid-to-turn missile demonstrates the performance of the proposed acceleration autopilot with the roll stabilization control.

APPLICATION OF 1H-NMR SPECTA AND MULTIVARIATE ANALYSIS FOR THE AUTHENTICATION OF CURCUMA XANTHORRHIZA FROM ZINGIBER CASSUMUNAR

Objective: This study was aimed to apply metabolite fingerprinting for the authentication of Curcuma xanthorrhiza adulterated with Zingiber cassumunar using 1H-NMR spectroscopy and multivariate analysis (chemometrics) methods, namely principal component analysis (PCA) and partial least square–discriminant analysis (PLS-DA).
 Methods: The pure dried powder samples of C. xanthorrhiza from different regions, Z. cassumunar, and its binary mixtures of C. xanthorrhiza with various concentrations of Z. cassumunar as adulterants were prepared for 1H-NMR measurements. The binary mixtures were prepared by mixing C. xanthorrhiza with various concentrations (10%, 25%, 40%, 50%, and 75%) of Z. cassumunar. 1H-NMR spectra were subjected to multivariate analysis for classification using PCA and PLS-DA.
 Results: A diverse group of metabolites could be detected by 1H-NMR spectroscopy. PCA using the chemical shift in 1H-NMR spectra of the plant extracts as variables clearly discriminated pure C. xanthorrhiza extracts from different origins and C. xanthorrhiza extract adulterated with Z. cassumunar. PLS-DA employed to enhance the separation obtained from the PCA model resulted in well separation and good classification of pure C. xanthorrhiza from the adulterated ones.
 Conclusion: The developed method could be a useful and powerfull tools to assess adulteration practice and to evaluate the authentication of C. xanthorrhiza extracts.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

Some properties and applications of non-trivial divisor functions

The jth divisor function d_j, which counts the ordered factorisations of a positive integer into j positive integer factors, is a very well-known multiplicative arithmetic function. However, the non-multiplicative jth non-trivial divisor functionc_j, which counts the ordered factorisations of a positive integer into j factors each of which is greater than or equal to 2, is rather less well studied. Additionally, we consider the associated divisor functionc_j^{(r)}, for rge 0, whose definition is motivated by the sum-over divisors recurrence for d_j. We give an overview of properties of d_j, c_j and c_j^{(r)}, specifically regarding their Dirichlet series and generating functions as well as representations in terms of binomial coefficient sums and hypergeometric series. Noting general inequalities between the three types of divisor function, we then observe how their ratios can be expressed as binomial coefficient sums and hypergeometric series, and find explicit Dirichlet series and Euler products for some of these. As an illustrative application of the non-trivial and associated divisor functions, we show how they can be used to count principal reversible square matrices of the type considered by Ollerenshaw and Brée and so sum-and-distance systems of integers.

Zolotarev Iterations for the Matrix Square Root

We construct a family of iterations for computing the principal square root of a square matrix $A$ using Zolotarev's rational minimax approximants of the square root function. We show that these rational functions obey a recursion, allowing one to iteratively generate optimal rational approximants of $\sqrt{z}$ of high degree using compositions and products of low-degree rational functions. The corresponding iterations for the matrix square root converge to $A^{1/2}$ for any input matrix $A$ having no nonpositive real eigenvalues. In special limiting cases, these iterations reduce to known iterations for the matrix square root: the lowest-order version is an optimally scaled Newton iteration, and for certain parameter choices, the principal family of Padé iterations is recovered. Theoretical results and numerical experiments indicate that the iterations perform especially well on matrices having eigenvalues with widely varying magnitudes.

Imaging spectroscopic approach for land degradation studies: a case study from the arid land of India

Arid regions are composite of complex structures. Desertification and land degradation of these arid regions need appropriate measures in accordance with time. Hyperspectral remote sensing data sets have high potential capabilities in estimating land degradation phenomenon. Imaging spectroscopy appears to be an emerging technology for the assessment of soil properties which directly influences land degradation. In this study, soil properties were studied from the arid region in Jaisalmer, Rajasthan, India using low bandwidth data like Hyperion along visible, near-infrared and shortwave infrared (350–2500 μm) region of the electromagnetic spectrum. Spectral reflectance-based algorithms have been applied for the assessment of soil resources of arid regions. The results showed that sand, clay and organic matter content can be adequately estimated from linear models such as principal components and partial least square regression-based model over band reflectance derived from the ground observation and satellite imagery. This study might help in rapid assessment of soil properties in arid regions which might help us in periodic assessment of degradation over any region.

The Structured Condition Number of a Differentiable Map between Matrix Manifolds, with Applications

We study the structured condition number of differentiable maps between smooth matrix manifolds, developing a theoretical framework that extends previous results for vector subspaces to any smooth manifold. We present algorithms to compute the structured condition number. As special cases of smooth manifolds, we analyze automorphism groups, and Lie and Jordan algebras associated with a scalar product. For such manifolds, we derive a lower bound on the structured condition number that is cheaper to compute than the structured condition number. We provide numerical comparisons between the structured and unstructured condition numbers for the principal matrix logarithm and principal matrix square root of matrices in automorphism groups as well as for the map between matrices in automorphism groups and their polar decomposition. We show that our lower bound can be used as a good estimate for the structured condition number when the matrix argument is well conditioned. We show that the structured and unstructured condition numbers can differ by many orders of magnitude, thus motivating the development of algorithms preserving structure.

Chitosan-based nanocomposite matrices: Development and characterization

Chitosan-based nanocomposites have a significant industrial impact related to the possibility to design and create new materials and structures. Cellulose nanocrystals (CNC) can be extracted from microcrystalline cellulose (MCC) by controlled acid hydrolysis with H2SO4. This work was focused on: to study the microstructure of CNC isolated from MCC after different hydrolysis times; to develop nanocomposites chitosan-based films; to characterize their structural and thermo-mechanical properties; to analyze the spectral differences among samples by means of ATR-FTIR in combination with principal component analysis (PCA) and square partial minimums model (PLS).It is worth noting that the selected condition for isolate the CNC from MCC was the acid treatment for 2 h, evidenced by size measurements. This fact was supported by transmission electron microscope (TEM) and dynamic light scattering (DLS).In this regard, SEM studies of films showed an assembly process between the nanocelluloses and the CH matrix. The incorporation of CNC into the films resulted in strong interactions between the filler and the matrix demonstrating the affinity between the phases and modifying the mechanical profiles.In summary, CNC was found to be a satisfactory reinforcing agent in biodegradable nanocomposite chitosan-based packaging and are promising as a means to develop tailor-made materials.

Comparison of data science workflows for root cause analysis of bioprocesses

Root cause analysis (RCA) is one of the most prominent tools used to comprehensively evaluate a biopharmaceutical production process. Despite of its widespread use in industry, the Food and Drug Administration has observed a lot of unsuitable approaches for RCAs within the last years. The reasons for those unsuitable approaches are the use of incorrect variables during the analysis and the lack in process understanding, which impede correct model interpretation. Two major approaches to perform RCAs are currently dominating the chemical and pharmaceutical industry: raw data analysis and feature-based approach. Both techniques are shown to be able to identify the significant variables causing the variance of the response. Although they are different in data unfolding, the same tools as principal component analysis and partial least square regression are used in both concepts. Within this article we demonstrate the strength and weaknesses of both approaches. We proved that a fusion of both results in a comprehensive and effective workflow, which not only increases better process understanding. We demonstrate this workflow along with an example. Hence, the presented workflow allows to save analysis time and to reduce the effort of data mining by easy detection of the most important variables within the given dataset. Subsequently, the final obtained process knowledge can be translated into new hypotheses, which can be tested experimentally and thereby lead to effectively improving process robustness.