Multidimensional Method Research Articles

Characterization of peptide O⋯HN hydrogen bonds via1H-detected 15N/17O solid-state NMR spectroscopy.

High sensitivity and resolution solid-state NMR methods are reported, that straightforwardly select hydrogen-bonded 15N-17O pairs from amongst all other nitrogen and oxygen sites in peptides, to aid protein secondary and tertiary structure determination. Significantly improved sensitivity is obtained with indirect 1H detection under fast MAS and stronger relayed dipole couplings.

Multidimensional data amplification method for continuous monitoring of subdural hematomas

Multidimensional data amplification method for continuous monitoring of subdural hematomas

A Corpus-based Multi-dimensional Analysis on Chinese and US Corporate Responsibility Reports: A Case Study of HUAWEI and Apple

The discourse of Corporate Social Responsibility (CSR) report can predict the performance of CSR. Research on the CSR report of international well-known companies can provide reference and encouragement for others listed companies. Based on the multi-dimensional analysis method of Biber, this study investigates the linguistic features of the CSR report discourse of Huawei and Apple. This study makes a comparative analysis of the CSR discourse from the two firms, to explore the differences in discourse functional dimensions between Chinese and American corporate CSR reports. This study finds that, on the one hand, there are differences between Chinese and American CSR reports in dimensions of Involved versus Informational Production, Explicit versus Situation-dependent Reference, and Abstract versus Non-abstract Information. Compared with Apple’s CSR reports, Huawei’s CSR reports are more informative and explicit, but less interactive and abstract. On the other hand, there is no significant difference between Huawei’s and Apple’s CSR reports in Narrative versus Non-narrative Concerns and Overt Expression of Persuasion dimensions, indicating that CSR reports are less narrative and persuasive in both Chinese and American firms. This study has implications for improving the quality of Chinese enterprises’ CSR reports and enlightenment value for corporations to improve social responsibility performance.

The influence of soft and hard quality management practices on quality improvement and performance in UAE higher education

The present research examines the debated relationship between quality management and innovation using a multidimensional quality management perspective. The quality performance that is supposed to result from the adoption of quality management is investigated further as a possible mediator between quality management and innovation in the higher education sector. The data needed to test the hypotheses was gathered by sending a survey via the internet to the faculty members at universities in the United Arab Emirates. Applying the approach of structural equation modelling with partial least squares, the hypothesised associations between 175 respondents are evaluated. According to the findings, implementing rigorous quality management has a direct as well as indirect impact on innovation performance via its impact on quality performance. The impacts of soft quality management on hard quality management have indirect consequences on innovation performance. The association between rigorous quality management and innovation performance is moderated in part by quality performance. This study provides one of the initial studies to apply the multidimensional method of quality management in higher education and has the potential to assist directors in better comprehending the interdependencies between soft and hard quality practices.

Discrete symmetry tests using hyperon-antihyperon pairs

The BESIII experiment has accumulated millions of spin entangled hyperon–antihyperon pairs from the decays of the J/ψ and ψ(2S ) mesons produced in electron–positron collisions. Using multi-dimensional methods to analyze the angular distributions in the sequential decays the hyperon and antihyperon decay amplitudes are studied and tests of the combined charge-conjugation–parity (CP) symmetry are performed. Implications of the recent results using J/ψ decays into Λ$ \bar \Lambda $ and Ξ−$ \bar {\Xi}^{+} $ are presented. For the cascade decay chain, the exclusive measurement allows for three independent CP tests and the first direct determination of the weak phases.

Application of Multidimensional Statistical Methods to the Hydrochemical Study with R Software

Conducting extensive, time-consuming analysis campaigns is a typical technique to better understand and manage surface water quality. These usually generate a substantial amount of data that is challenging to comprehend. Principal component analysis may be advantageous for such a project (PCA). From the perspective of such an application, eight physico-chemical parameters are important: Sodium (Na+), Bicarbonate (HCO3-), Magnesium (Mg2+), Total Alkalinity (as CaCO3), Chlorides (Cl-), Potassium (K+), Calcium (Ca2+), Sulfates (SO42-), coming from the analysis of 100 water samples collected between February 2014 and December 2015 on 25 stations distributed on Inaouen catchment areas, were analyzed. The principal component analysis applied to the data showed that the variables could be grouped into two principal components. The interpretation of the results using these tools allowed us to understand that the parameters responsible for water quality are related to component Dim1 (HCO3-, CaCO3, K+, Cl-, Na+ and SO42-) and component Dim2 to the processes associated to (Ca2+ and Mg2+) for the physicochemical parameters, the Dim1 factorial design accounts for 67.80% of the variance; it is expressed towards its positive pole by HCO3-, CaCO3, K+, Cl-, Na+ and SO42-, which present good correlations between them. However, the Dim2 factorial plane represents only 17.60%, defined by the Ca2+ and Mg2+ ions towards its positive pole. The Dim1XDim2 plane's typological structure reveals the individualization of three different groupings based on their hydrochemical quality. A feasible reduction in the number of dimensions without a major loss of information was discovered by the PCA. This tool is a good choice from the standpoint of developing management tools.

Feasibility of the Goal of Sociology under Its Current Methodology

Sociology has attained a prominent status in the modern world, due to the role it played in the analysis of contemporary social issues and events. This has attracted an increasing interest in sociology not only in the Westerns societies, but also in the Muslim world due to their desire for modernization. Therefore, this article attempts to analyse whether sociology can attain its goal of reorganizing society, based on scientific methods. It also analyses ethics and morality as alternative means for the purpose, and as a source of derivation of universal rule of human social behaviour hence, reorganization of society. This research is theoretical and philosophical and uses a qualitative method as it relies on the analysis of the data gathered from textual sources. Using an analytical approach, the writers arrive at the conclusion that Sociology under its current scientific methodology, cannot achieve its goal of reorganizing society. This can be attributed to inadequacy of scientific method in the study of human society, due to the unpredictability of human behaviour and variety of its factors. The writers also suggest the adaptation of a dynamic multidimensional method that consider variety of factors such as spiritual, physical, mental, social, and moral which heavily influence human behaviour.

Disparities in the level of regional technical infrastructure development in Poland: multicriteria analysis

Research background: The development policy currently promoted by the European Union is focused on the use of the territory?s internal resources. Among the factors affecting regional development, by building its potential, infrastructure, being a basic necessity for developing activity in a given area, is of significant importance. Hence, investment in infrastructure is critical to stimulating economic dynamism, as it is the basis for supporting a variety of measures aimed at economic growth. Purpose of the article: This paper aims to evaluate the level of development of technical infrastructure and changes taking place in this field in Polish voivodeships in 2008 and 2020. Methods: The study was carried out using the Hellwig development pattern method and a comparative analysis of the technical infrastructure of Polish regions. The above approach makes it possible to measure the diversity of the state and availability of infrastructure for the communities of the regions. Findings & value added: While implementing the study aim, particular attention was paid to the spatial differences in the level of development of the technical infrastructure of Polish voivodeships. The analysis enabled to distinguish groups of voivodeships with the highest, high, low, and very low level of technical infrastructure development. From a long-term perspective, the conducted research can be seen as a contribution to existing research and serve to further compare the impact of technical infrastructure on the economic development of countries. The strength of the study is the adequately long time span of the analysis (2008 ? the period of the financial crisis and 2020 ? the COVID 2019 pandemic), which provides a basis for the formation of the infrastructure in question. The added value of the article is also a regional perspective on the level of development of technical infrastructure using multidimensional methods of statistical analysis. The results of the study can be used to make decisions at the national level regarding the retrofitting of infrastructure in regions with a low level of infrastructure development. For the European Union's decision-makers they can be a source of knowledge of where to direct EU funds the purpose of which is the infrastructural development of regions.

Generative adversarial networks for modelling clinical biomarker profiles with race/ethnicity.

Modelling biomarker profiles for under-represented race/ethnicity groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. The aim was to investigate generative adversarial networks (GANs), an artificial intelligence technology that enables realistic simulations of complex patterns, for modelling clinical biomarker profiles of under-represented groups. GANs consist of generator and discriminator neural networks that operate in tandem. GAN architectures were developed for modelling univariate and joint distributions of a panel of 16 diabetes-relevant biomarkers from the National Health and Nutrition Examination Survey, which contains laboratory and clinical biomarker data from a population-based sample of individuals of all ages, racial groups and ethnicities. Conditional GANs were used to model biomarker profiles for race/ethnicity categories. GAN performance was assessed by comparing GAN outputs to test data. The biomarkers exhibited non-normal distributions and varied in their bivariate correlation patterns. Univariate distributions were modelled with generator and discriminator neural networks consisting of 2 dense layers with rectified linear unit-activation. The distributions of GAN-generated biomarkers were similar to the test data distributions. The joint distributions of the biomarker panel in the GAN-generated data were dispersed and overlapped with the joint distribution of the test data as assessed by 3 multidimensional projection methods. Conditional GANs satisfactorily modelled the joint distribution of the biomarker panel in the Black, Hispanic, White and Other race/ethnicity categories. GAN is a promising artificial intelligence approach for generating virtual patient data with realistic biomarker distributions for under-represented race/ethnicity groups.

Data reconstruction combining MWNI and CRS-based interpolation methods

Many seismic processes, such as migration, inversion, and quantitative interpretation, can be improved via multidimensional regularization and interpolation methods. Different algorithms based on signal processing principles have been proposed for multidimensional seismic data reconstruction. Similarly, the Common Reflection Surface (CRS) stack method, a technique based on ray-theory principles, can also be used for regularization and interpolation. CRS-based reconstruction requires determining the wavefront attributes of the CRS stacking operator directly from the data. These wavefront attributes are commonly searched through global optimization algorithms, where a coherence measure is maximized. The precision and reliability of the wavefront attributes (also called CRS attributes) depend on the optimization method used in their search and the quality of the input data. Onshore field data with missing traces and gaps due to missing shot records can introduce attribute errors and create artifacts that contaminate seismic stacks. To solve this problem, we propose applying the Minimum Weighted Norm Interpolation (MWNI) method for the reconstruction of the prestack data before estimating the CRS attributes. Then we use the wavefront attributes to CRS-based prestack data reconstruction and denoising. We validate the combination of MWNI and CRS-based interpolation methods with synthetic data and show that the CRS attributes extracted by the combined approach are very similar to the reference values. The proposed processing flow shows excellent results on an onshore vintage dataset from the Parnaíba Brazilian basin, where MWNI operates as a fold regularization. The reconstructed data obtained by combining MWNI and CRS show significant improvements compared to the reconstructed data using the two algorithms separately. Prestack time migration images show increased quality when the two methods are adopted in the processing flow.

Sustainable poverty alleviation capacity construction of farmers in poverty-stricken areas under the background of rural revitalization.

Rural revitalization aims to consolidate the achievements of poverty alleviation. Furthermore, enhancing the sustainable poverty alleviation ability of farmers in poverty-stricken areas is a critical concern. Using samples of farmers in four regions of the Yunnan province in China, multidimensional poverty and sustainable poverty alleviation ability indexes were constructed. Furthermore, a multidimensional poverty double boundary method (A-F method) was used to identify multidimensional poverty, and binary logistic function in SPSS was used to perform a regression analysis between multidimensional poverty and sustainable poverty alleviation ability. Results showed that as the poverty dimension increased, poverty incidence rate in the four regions decreased, with middle poverty incidence rate being the highest. Ability poverty was then identified as the major type of poverty in the sample, with the group of poor farmers with a high dimension of poverty being the most affected. Therefore, based on the ability structure of sustainable farmers out of poverty, we analyze the prominent problem of the ability construction of sustainable poverty reduction and propose a path for promoting sustainable poverty reduction ability. Hence, we will effectively link poverty alleviation with rural vitalization.

Investigation of E-27 and Candidate Countries’ Lifelong Learning, R&D–Innovation Performances and Gross Domestic Product by Multidimensional Scaling Analysis

Vocational and technical education, which is offered in the quality required by the era, plays a major role in the growth of economies and the development of societies by raising entrepreneurs with R&D, P&D, and innovation awareness, as well as being a means of raising a productive workforce in a systematic and planned way. The lifelong learning journey also offers a great contribution to this process. The aim of this study is to evaluate the effects of these factors on the economies of countries by comparing the lifelong learning, entrepreneurship, and innovation performances of countries with their per capita gross domestic product within the scope of both the data in the Global Innovation Index (GII) report and the data in the European Innovation Index scoring table. Ideas produced by using information-intensive innovative technologies and the products that emerge with the realization of these ideas contribute to economic growth by strengthening the competitiveness and economic structure of countries, thus, increasing living standards and increasing welfare. In this respect, it is important to measure and evaluate the lifelong learning, R&D, and innovation performances of countries. A multidimensional scaling analysis method was used in the study. In the analysis of data and documents obtained, it has been found that the countries that construct and carry out their vocational education and training activities according to the realities and needs of today’s world, have high R&D, innovation, and lifelong learning performances. It has been seen that the value added created in this way is also high and that has a decisive effect on their economic growth.

Physical and chemical evolution of sugar beet pulp during aqueous ammonia pretreatment and its impacts on the recalcitrance of cellulose

Recognition and deconstruction of biomass recalcitrance are essential steps in fuel ethanol production. Aqueous ammonia (AA) is an efficient pretreatment to improve the enzymatic digestibility of sugar beet pulp (SBP), but the precise mechanism is still ambiguous. Multidimensional methods, e.g., physical properties, wet chemistry compositions, and glycome profiling were integrated to elucidate the impacts of AA pretreatment on recalcitrance of SBP semi-quantitatively. Additionally, the variations in enzyme adsorption and cell wall morphology during the enzymatic hydrolysis were systematically observed. The results confirmed that the constitution, association, and distribution of glycan epitopes within cell walls significantly correlated to recalcitrance. Specifically, the arabinogalactan and rhamnogalacturonan epitopes negatively correlated to the recalcitrance. And xylans only bound to cellulose contributed to recalcitrance. Dislocation and fragmentation of cell walls through sequential removal of several glycan epitopes could facilitate enzymatic digestibility, in terms of enzyme adsorption, reducing sugar yields, and enzymatic saccharification behaviors. Whereas, cell corners seemed to be highly resistant to enzymatic digestion due to some gathered epitopes. The maximum reducing sugar yields ranging from 437.7 to 441.48 mg/g were obtained from the pretreated SBP (3, 5, and 7 h), which nearly increased by 87% compared to the control. Selecting a proper pretreatment to precisely strip out the vital glycan epitopes might be an economically feasible strategy in fuel ethanol production.

Power Flow Analytical Method for Three-Phase Active Distribution Networks Based on Multi-Dimensional Holomorphic Embedding Method

With the increasing penetration of distributed generations (DGs) in three-phase distribution networks, the traditional radial passive distribution networks are gradually transformed into active distribution networks (ADNs) with DGs, accompanied by a dramatic increase in operational scenarios. To fast analyze the massive scenarios of ADNs, this brief proposes a multi-scenario-based power flow analytical method based on the multi-dimensional holomorphic embedding method. First, DGs are partitioned according to their spatial correlation, and then a power flow analytical method is cast for unbalanced three-phase ADNs. Finally, the method is applied to the ADN power flow analysis for multiple scenarios. Comparisons with other power flow calculation methods validate the high efficiency and computational tractability of the proposed method.

Statistical Modelling of Factors Influencing the Agricultural Land Market in Ukraine

Abstract To make informed decisions, modern society, like modern business, must operate with adequate information about many complex interrelated aspects of its activities. Land use is only one of such aspects. Agricultural lands are of particular importance today. Land data are needed to analyse environmental processes, as well as end unsystematic, uncontrolled use of agricultural land, environmental degradation, destruction of important wetlands, loss of fish diversity and destruction of wildlife habitats. To solve these problems, it is advisable to conduct regular analysis and evaluation of land resources, justification and analysis of factors influencing the agricultural land market in the country. The tasks were solved using multidimensional statistical methods, in particular, factor analysis, which helped to get rid of subjectivity in the choice of factors influencing the market under study. Based on the model of influence of factors on the national market of agricultural land in Ukraine, six main factors were identified, the most influential of which were the components of agricultural development (first factor) and, to a lesser extent, the component of vegetable production and yield (fourth factor). The interrelationships and the degree of influence of the selected factors on the main indicators of the agricultural land market, namely, on the area of purchase and sale plots, the price of purchase and sale plots, and the number of transactions were also analysed.

Microbial Diversity of Six Commercially Available Kefir Grains

Abstract Natural kefir grains are rich in beneficial bacteria, and analysis of their microbial diversity is a necessary condition for developing and applying kefir grains. In this study, six commercially available natural kefir grains were used as raw materials to explore their microbial diversity by metagenomics. The results showed that there were 14794 genes in 6 kinds of natural kefir grains, and the number of unique genes of X1, X2, X3, X4, X5, X6 were 111, 11, 0, 1899, 552, 1, respectively. From the relative abundance table of boundary, phylum, class, order, family, genus and species, the microbial diversity at each level was analyzed.The two dominant genera at the genus level are Lactobacillus and Lactococcus, and the dominant species at the species level are Lactococcus lactis and Lactococcus kefiranofaciens, Lactococcus crispatus, and Lactococcus helveticus, etc. Species distribution and species diversity of each sample were analyzed by species heat map, principal component analysis and non-metric multidimensional calibration methods. The results showed that the microbial diversity of natural kefir grains from 6 different sources were different. The research can provide reference for the development and application of natural kefir grains in the field of dairy products.

Assessment of distributed new energy-bearing capacity of new power systems with multisource complementary characteristics

A reasonable assessment of the bearing capacity of distributed power-generation sources in power systems is a key guarantee for the construction of new power systems with new energy sources as the core. In view of the fact that the current distributed new energy-bearing capacity assessment method has not yet established a systematic assessment index system and has not taken into account multisource complementarity, a new distributed energy-bearing capacity assessment method for power systems that takes into account the characteristics of multisource complementarity was proposed. First, a distributed new energy-bearing capacity assessment framework was constructed and the influencing factors of distributed energy-bearing capacity were analysed. Second, a multidimensional distributed power bearing-capacity assessment index system and calculation method considering thermal stability, short-circuit current, voltage deviation, and harmonics was proposed and the assessment process of distributed energy-bearing capacity was proposed. Finally, the validity and applicability of the proposed method were verified through actual case simulation.

Contextualized Recommendation of Aviation Ancillary Services Based on Passenger Portraits

Ancillary services are crucial to Chinese domestic airlines’ profits. Personalized recommendations for ancillary aviation services must consider massive amounts of contextualized online and offline data. The issue of sparsity is more serious in massive data, which makes traditional collaborative filtering (CF) algorithms less accurate. This article adds instant contextualized travel-related factors to the modeling of air passengers’ ancillary services, thus achieving dynamic recommendations. It also proposes a four-tuple to construct a contextual ontology as a conceptual model of ancillary aviation services. The article establishes a context-aware air passenger portrait model based on four tag sets, including passenger attribute tags, ancillary service attribute tags, passenger interaction behavior tags, and contextual attribute tags. Further, it proposes a multidimensional method for calculating contextual portrait similarity; the method recommends a list of the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> items to the target passenger according to the similarity calculation. Compared with the traditional CF recommendation method, the proposed algorithm has better performance, improving the accuracy rate by 4.3% and the recall rate by 2.62%. The final experimental result demonstrates that the contextual recommendation method achieves higher passenger satisfaction, which can provide consolidated data and decision support for the management of aviation ancillary services.

혁신학교 정책에 대한 다차원적 IPA 분석

Purpose: The purpose of this study was to evaluate the Innovation School policy that has spread nationwide since 2009 through Cooper’s multidimensional policy analysis method. Based on this, the future direction of the Innovation School was discussed. Methods: A survey was conducted on elementary, middle, and high school teachers’ perceptions in area A, which has been operating the Innovation School since 2015. By applying a four-dimensional model as a tool for evaluating policy, the Innovation School policy was divided into normative-dimension, structural-dimension, constituentive-dimension, and technical-dimension domains. Based on this, the importance-performance analysis (IPA) was performed. Results: As a result of the study, democratic management was recognized as the most urgent and intensive factor to be supplemented. In addition, the importance and performance were rated the lowest for connection with the local community. In addition, the participation and role of staff and parents in the management of the Innovation School were found to be insignificant. And teachers’ perceptions pointed out the ambiguity of the roles of school supervisors and demanded that the meaningless evaluation be avoided. Conclusion: Teachers' perceptions can be perceived as a threat to the sustainability of the Innovation School. The local community and several school members were excluded from the Innovation School policy which was aimed at the broader school community. The question of democracy was also being raised, and this area can be evaluated as requiring another innovation.

Multi-dimensional particle filter-based estimation of phase line biases for single-differenced ambiguity resolution in GNSS-based attitude determination

Global navigation satellite systems (GNSSs) have been widely used to provide real-time and high-precision attitude information for land vehicles, ships and aircraft over the past few decades. With the joint use of emerging multi-GNSS common-clock receivers and the single-differenced (SD) model, the accuracy of pitch and roll can be significantly improved to the same level as that of yaw. However, the prerequisite is that the frequency-dependent phase line biases (LBs) in multiple GNSS systems and frequencies are accurately and rapidly estimated. In this contribution, we intend to solve this problem by using a multi-dimensional particle filter (PF)-based approach. We first investigate the relationship between the ratio value and the multi-dimensional phase LBs. Results have revealed that the ratio value can be used to judge the quality of multi-dimensional phase LBs and represent the likelihood function of the observations. We then present the procedure of multi-dimensional PF-based phase LBs estimation for SD ambiguity resolution and attitude determination. An improved strategy is also proposed to reduce the computation time. Finally, we take the two-dimensional case as a representative example to evaluate the performance of the proposed method in aspects of the convergence and accuracy of phase LB estimates, the attitude determination accuracy, and the computation time. Experimental results from two static datasets have demonstrated that the two-dimensional phase LBs basically rapid converge within 20 epochs. Moreover, compared with the double-differenced method, the proposed multi-dimensional PF-based SD method could provide comparable yaw accuracy and much better pitch accuracy. The pitch accuracy is improved to the same level as yaw by approximately 42.9%–50.0%. With regard to the computation time, it is found that with the proposed modification strategy, the single-epoch computation times are significantly reduced by approximately 90.7%–93.5%, and they are mostly within 0.05 s for most of the epochs on a personal computer.