Multidimensional Method Research Articles

Sustainable Development Efficiency of Cultural Landscape Heritage in Urban Fringe Based on GIS-DEA-MI, a Case Study of Wuhan, China.

Cultural landscape heritage refers to the rare and irreplaceable cultural landscapes recognized by UNESCO and the World Heritage Committee. It is recognized as a “common works of nature and human beings” of outstanding significance and universal value, and is a type of world heritage. Dueto construction, land isincreasingly limited in urban and rural areasin the process of urbanization, and cultural landscape heritage faces a huge threat, especially larger culturallandscapeheritagelocated at the edgesof cities. However, most of the existing studies have mainly focused on the material protection of heritage but have not paid enough attention to the non-material aspects of heritage sites, failing to reveal the inseparable nature of heritage and land. Therefore, this study takes sustainable development efficiency as its analysis tool, examines two pieces of cultural landscape heritage (the Panlongcheng site and the Tomb of the King of the Ming Dynasty) in the urban edge area of Wuhan, China as examples, innovates and establishes a multidimensional evaluation method based on the GIS-DEA-Ml model, and compares the dynamic changes of the spatial development efficiency and non-spatial development efficiency of the above two cultural landscape heritage cases. The results show that: both the spatial development efficiency and non-spatial development efficiency of Panlongcheng from 2010 to 2019 are significantly higher than that of the Tomb. This method makes up for the deficiency of traditional subjective qualitative analysis. It can be used to study the development efficiency of cultural landscape heritage more objectively and comprehensively, and promote the overall sustainable development of material and intangible cultural heritage. It can provide the basis for early decision-making and post-implementation evaluation for the preservation and utilization of cultural landscape heritage under the background of urban renewal.

Dynamic response of multi-scale geophysical systems: waves and practical applications.

In this review paper, we discuss the nature of an apparent link between heterogeneities associated with geological anomalies hidden from direct observation, detected when they are analysed remotely from various distances-including borehole conditions as well as aero imaging. Here, the main emphasis is placed on geological and geophysical features represented by spatially distributed signals measured along drilled well or along predefined spatial routes. In the common practice of indirect measurements of parameters of seismically active regions, there exist certain observations on correlations in the vicinity of structural and geological anomalies, repeated patterns in the representation of correlation functions and corresponding classification in multidimensional statistical methods. Underlying natural physical processes, which determine the structure of the primary anomalous environment, are of interest. Physical analogies, based on the mathematical modelling and generalization of empirical data, may suggest that such a process may be linked to wave phenomena on a geological scale. Applications include analysis of anomalies associated with non-potential fields, mapping of geodynamic zones and seismic microzoning; reconstruction of the geostructural vertical section of the mountainous regions. This article is part of the theme issue 'Wave generation and transmission in multi-scale complex media and structured metamaterials (part 2)'.

Effect of sono-pre-texturization on β-lactoglobulin-anthocyanins energy appetizers

The occurring of glycation reaction and protein-protein interaction in the energy appetizers caused browning and hardness instability while storing these appetizers, leading to the loss of consumer acceptability. Amassing among anthocyanins and proteins could mitigate the appetizers' instability. We, therefore, investigated the anti-aggregation and ant-glycoxidation impacts of mulberry anthocyanins combined with ultrasonic treatment (UT) pre-texturization in an energy appetizer model throughout storage via multi-dimensional methods, containing UPLC-ESI-MS/MS, SDS-PAGE, FTIR, texture analyser, and a molecular docking analysis. Results noted that UT-anthocyanins significantly downgraded the browning progress, advanced glycation end-products, and/or N-(carboxymethyl)-l-lysine intensities of energy appetizers after 45 d of storage at 45 °C. UT-anthocyanins also relegated the protein insolubility, accumulation, oligomerization, and glycoxidation throughout the late storage. A molecular docking analysis evidenced that cyanidin 3-O-glucoside and cyanidin 3-O-rutinoside networked with β-lactoglobulin subunits via H-bonding and π-π forces. This binding hindered some glycoxidation residues of β-lactoglobulin the lysyl residues. Finally, these findings recommended that the UT-anthocyanins could be employed as an encouraging antiglycative approach to alleviate AGEs-creation and other consequent undesirable fluctuations in protein-rich food patterns, thereby enhancing the energy appetizer's post-processing stability during storage.

Development and Effect of Child Obesity Management Program by Applied Nudge.

Background: Child obesity rates are increasing worldwide. In Korea, the proportion of overweight students has steadily increased from 21.8% in 2015 to 25.8% in 2019. Childhood obesity causes mental problems, such as depression and social phobia, due to mental stress, feelings of inferiority, and low self-esteem. Methods: This experimental study aimed to verify the effect of the child obesity management program on body changes (height, weight, obesity degree, body mass index [BMI], body fat percentage), eating habits, exercise habits, obesity knowledge, and social support. This child obesity management program applies the nudge technique based on an ecological model and induces autonomous weight management through environmental control. Results: As results of this study, the child obesity management program using the nudge technique developed in this study improved the height (t = −5.19, p < 0.001), obesity degree (z = −3.28, p = 0.001), BMI (z = −3.22, p = 0.001), exercise habits (t = −2.09, p = 0.040), and obesity knowledge of obese children (z = −2.99, p = 0.003). Conclusions: This multidimensional intervention improved obesity by inducing and sustaining behavioral changes in obese children. Therefore, applying the nudge techniques and multidimensional intervention methods based on ecological model are proposed to increase the effectiveness of the health promotion programs.

상권 방문특징과 상권 포지셔닝 맵에 대한 탐색적 연구: 대응일치분석과 다차원척도법의 절차적 적용

The process of identifying the characteristics of the trading area to be analyzed and analyzing the difference from the competitive trading area is an essential prior task in establishing marketing strategies to revitalize a trading area. However, most trading area studies, so far, have relied heavily on quantitative descriptive statistics of a trading area itself or the empirical insight of researchers. As a result, objective and valid research methodologies for analyzing the characteristics of trading area were very deficient and there were few research attempts to analyze differences from competing trading areas based on the perception differences of visitors. To achieve the objectives of this study, the following procedures were carried out. First, a questionnaire survey was conducted for consumers who frequently or accustomed to visiting the trading areas of Konkuk University Station (K TA), Hongik University Station (H TA), and Gangnam Station (G TA), which were selected as the target trading areas. Next, through using the chi-square test, it was verified whether there were significant differences between the three trading areas and the eight categories, such as the origin of the visit and the type of visiting companion. The differences in the association of visitors between trading areas and categories were plotted in diagrams. And by using the Multiple Correspondence Analysis (MCA), which is an extension of CA, the perception differences of visitors were visualized on the positioning map with respect to the relationship between the three trading areas and gender/type of visit companion, and gender/age group. In addition, by using the Multidimensional Preference Analysis (MDPREF), one of the types of Multidimensional Scale Methods (MDS), the difference in perception of visitors is analyzed through a positioning map on the relationship between three trading areas, the purpose of the visit, major use business types, and information sources. A procedural methodology for the creation of a trading area positioning map was presented. The results are as follows. First, there were significant differences in the categories of the origin of visit, type of visiting companion, average spending amount, visiting transportation, and return transportation in the three trading areas. Second, the MCA results verified that the K TA is is favored by the female and the 20s, the H TA by the male and the 30s, and the G TA by the 40s. Finally, the results of the MDPREF analysis are as follows. First, in terms of the perception of visitors, the H TA was recognized as a mixed trading area with multi-dimensional purposes, not specific factors, unlike the other two trading areas. Second, in the relationship between the three trading areas and the major business type used, the three trading areas were not differentiated and were found to be in fierce competition. Third, in terms of information sources, there was no difference in the information sources used by visitors to the three trading areas, and it was found that visitors preferred to obtain informations through Internet search, neighborhood recommendation, and SNS. The results provide trading area marketers with a theoretical methodology to build effective trading area marketing strategies including market segmentation and customer targeting through the objective identification of visit characteristics and analysis of perceptual differences of trading area visitors. In addition, the results suggest a theoretical and procedural methodology for arranging the trading area positioning map that is differentiated as a competitive strategy and repositioning the specific trading area by improving the visitor

Solving magnetic induction heating problem with multidimensional Fredholm integral equation methods: Alternative approach for optimization and evaluation of the process performance

Induction heating is a frequently used technology in both fundamental and applied research. It is heavily exploited in the industry for processing materials by heat treatments. In addition, it is viewed as a promising tool in medicine, particularly as a part of therapeutic strategies for treating cancer diseases. Thus, in order to optimize (i.e., enhance and tune) the performance of the induction heating process, several aspects must be considered, including the design of the magnetic coils, features of the magnetic fields applied, coupling of magnetic and thermal fields, and the material’s characteristics. To tackle this complex problem, numerical mathematical models are often used. The results of which can help in understanding the role of the various parameters on the performance of the induction heating. Here, we present an alternative mathematical approach to solve the induction heating problem using Fredholm integral equations of the second kind with a singular kernel. To reduce the computation time, the Nyström method has been adopted. As the kernel function shows a singularity, a singularity subtraction has been involved in the developed mathematical procedure. Furthermore, the error features of the Nyström method with the singularity subtraction have been described, and convergence conditions of the proposed computational algorithm have been thoroughly identified. Although special conditions for the kernel function and the integration rule are needed, the method shows lower computing times, competing well with those of traditional finite-element based routines. The applicability of the developed methodology is demonstrated for the simulation of induction heating the body of a metal object.

Micro-tomographic and infrared spectral data mining for breast cancer diagnosis

This study aimed to introduce a multidimensional data mining method of breast cancer (BC) diagnosis using the X-ray phase-sensitive microtomography (XPCT) and Fourier transform infrared spectroscopy (FTIR), which is a comparable approach to clinical histopathologic examination (H&E) and blood biochemical tests for achieving a simple in-situ quantitative diagnosis. Based on the high brilliance synchrotron radiation, nondestructive three-dimensional (3D) micro-tomograms of tissues and infrared absorption spectra and mapping of representative biomacromolecules of tissue and blood samples were obtained with higher signal to noise ratios (SNRs). The comparative analysis had been made between in-situ digital sections and stained histological sections. The XPCT images accurately visualized the micromorphology of tumor lesions, including fat granule degenerations, ductal proliferations, microcalcifications, collagen strands of dimensions less than 20 µm. The FTIR mapping and spectra effectively revealed the biological component distributions and tumor-related variations of lipids, proteins and nucleic acids between breast tumors and normal tissues, integrated with XPCT data into formation of complementary data. It enables to identify the complex biomolecular tumor markers, especially, the specific fine-structures of second derivative spectrum were found in 1400–1750 cm−1. Moreover, the attenuated total reflection FTIR (ATR-FTIR) data of fresh blood samples were collected to demonstrate spectral specificity and feasibility of machine learning (ML) classification for BC diagnosis compared to clinic blood biochemical tests. The random forest (RF) model was found to be the best model for differentiating BC with 100% accuracy. Therefore, the proposed methodology was proved to be a powerful tool to nondestructively visualize and classify soft tissue tumors.

Fission channels for fragment isotopes from Fl298 with magic nucleon numbers

Short-lived systems such as superheavy nuclei decay mainly by $\ensuremath{\alpha}$ emission. However, fission fragment emission is also probable, as theoretical calculations prove it. $^{298}\mathrm{Fl}$ dynamics decay is studied with a specialized binary macroscopic-microscopic method combined with the mass inertia tensor. The multidimensional WKB tunneling method is used to obtain the penetrabilities and half-lives. The calculations are applied to fission channels around the isotopes of two presumably favored fragments, Pb and Sn. The choice is due to their proton magicity shell correction favorable fission valleys.

Декларированные и латентные социальные группы: феномен, эволюция, детерминанты. Часть 1

Objective: to study the features of the social micro- and meso-environment as potential conditions for the formation, functioning and evolution of latent social groups. Hypotheses: 1. Various conditions of the social environment are not regarded as equivalent by people (persons of different genders, ages, working in different fields of activity, having different social experience, etc.). 2. The conditions of the social environment have different subjective significance for people and collectively represent relatively autonomous and closed social spaces. Methods: historical and theoretical analysis, empirical research (questionnaire), multidimensional methods of parametric statistics. According to the author’s methodology (questionnaire “Dynamics of professional life style” - DPLS), respondents assessed the role of social environment conditions as “factors of professionalism”, the dynamics of their professionalism from 20 to 65 years (retrospectively to the current age and prospectively – predicting the dynamics of subsequent evolution); their socio-demographic and official characteristics were also recorded. 482 employees, men and women aged 30-50 were interviewed (132 civil servants, 129 engineers and heads of departments of industrial enterprises, 221 managers). The results of the empirical study confirm the working hypotheses. Groups of conditions of different social spaces, conventionally called “Relatives”, “Interpersonal relationships”, “Family environment”, “Work environment”, “Meso-environment”, act as dominant or subdominant spheres of human activity, predetermining many differences between them. In the process of socialization, people form peculiar stable and universal mechanisms of activating and translating social experience; the lack of such experience often contributes to a person’s being less open and receptive to other conditions of the social micro- and meso-environment, to their being less socially successful. Briefly, the description and study of latent social groups is relevant since it helps to identify “subtle” mechanisms of self-organization of people into social communities.

Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach

In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporating near-infrared (NIR) spectroscopy and spectral prediction model for rapid, repeatable, non-destructive, and low running costs quantitative analysis of FQPs is enjoying increasing popularity in the food industry. However, most existing spectrum-based prediction models are trained under a single-task learning framework, that is, a prediction model for each quality parameter and spectrum is constructed separately. This paradigm ignores possible connections among prediction tasks of different FPQs, which may result in the performance degradation of a single FPQ prediction model. This study proposes a novel multi-task genetic programming-based approach named EM4GPO for building multiple FQPs predictions simultaneously. In EM4GPO, the multi-dimensional trees are used to encode the raw NIR spectrum to shared features of multiple FQPs; for each FQP, a least square support vector regression (LS-SVR) modeling is performed on the shared features to obtain private features and prediction model; during the optimization process, a new algorithm is developed to optimize the previously obtained shared and private features, and LS-SVR prediction models through population evolution by combining the multidimensional multiclass genetic programming with multidimensional populations optimization method with nondominated sorting method. The proposed EM4GPO model is evaluated and compared with nine popular NIR prediction models using 10 NIR spectral datasets. The experimental results showed that EM4GPO outperformed other commonly used methods in all datasets which indicates that EM4GPO is competitive and effective in solving the problem of multiple FQPs predictions using the NIR spectrum.

HDMAX2: A framework for High Dimensional Mediation Analysis with application to maternal smoking, placental DNA methylation and birth outcomes

Background High-dimensional mediation analysis is an extension of unidimensional mediation analysis that includes multiple mediators, and is increasingly used in environmental epidemiology to evaluate the indirect epigenetic effects of environmental exposures on health outcomes. However, analyses involving high-dimensional mediators raise several statistical issues. While many methods have recently been developed to tackle those issues, no consensus has been reached about the optimal combination of approaches. Methods We developed HDMAX2, a new multi-step approach to mediation that combines latent factor regression models for epigenome-wide association studies with max-squared tests for mediation, and considers CpGs and aggregated mediator regions (AMR). HDMAX2 was carefully evaluated on simulated data, and compared to state-of-the- art multi-dimensional epigenetic mediation methods. Then it was applied to assess the indirect effects of exposure to maternal smoking (MS) on term birth weight (BW) and gestational age (GA) at delivery in a study of 470 women from the EDEN cohort. Results HDMAX2 resulted in increased power compared to state-of-the-art multi-dimensional mediation methods, and identified several AMRs not identified in previous mediation analyses of exposure to MS on BW and GA. The results provided evidence for a polygenic architecture of the causal pathway with an overall indirect effect of CpGs and AMRs of 44.5g lower BW (32.1% of the total effect size). HDMAX2 also identified AMRs having simultaneous effects both on GA and BW. Among the top hits of both GA and BW analyses, regions located in COASY, BLCAP and ESRP2 also mediated the relationship between GA on BW, suggesting a reverse causality in the relationship between GA and the methylome. Discussion This study brought up several statistical improvements of high-dimensional mediation analyses, which revealed an unsuspected complexity of the causal relationships between exposure to MS and BW at the epigenome-wide level. Mediation; high dimension; causal inference; epigenetics; DNA methylation; pregnancy

Short Report: Outcomes for siblings associated with sub-groups of autistic children with intellectual disability identified by latent profile analysis

BackgroundRecent research suggests that having a brother or sister with autism may contribute to increased positive or negative emotional or psychological impact on siblings. AimsTo use a novel multidimensional data analysis method to further understand outcomes for siblings of autistic children. Methods and Procedures318 siblings of children with a recorded autism diagnosis and an intellectual disability were included for latent profile analysis. Five variables (DBC disruptive and anxiety; VABS II communication, daily living, and socialization skills) were used to identify sub-groups of autistic children. Primary carers reported on sibling relationship quality (items from the Sibling Relationship Questionnaire warmth/closeness and conflict scales), and siblings’ behavioral and emotional problems. Outcomes and resultsThe profile groups differed in their levels of ID coupled with disruptive behavior, emotional problems and adaptive skills. Profiles included a severe ID, low behavior and emotional problems and low adaptive skills group; a group with mild ID coupled with high adaptive skills and low emotional and behavioral problems; and a mild ID group with high emotional and behavioral problems. Conflict in the sibling relationship differed across the profile groups (F (4304) = 15.13, p < .001). Conclusions and implicationsSiblings of autistic children with the highest support needs were reported to have the lowest conflict in their relationships. Conversely, siblings of the autistic children with the highest levels of externalizing behaviors and anxiety were reported to have the highest levels of conflict in the sibling relationship.

Time-stretch-based multidimensional line-scan microscopy

Time-stretch tomography imaging with ultrahigh frame rate is a breathtaking optical imaging method for acquiring large data sets for detection and classification of rare events. However, mechanical scans of both x and y dimensions are required to obtain tomography imaging. Here we introduce an ultrafast multidimensional time-stretch imaging method, only by one-dimensional scan, a multidimensional imaging can be obtained. An entire row of surface and depth information for a reflective sample is encoded onto the amplitude and frequency of a spatially dispersed ultrafast chirped beam, respectively. The present multidimensional line-scan microscopy approaches an ultrafast imaging system with micron level positioning accuracy in depth, three-dimensional surface information acquisition and tomography capability, which aimed at emerging applications for optical coherence tomography (OCT), light detection and ranging (LIDAR) and hyperspectral imaging.

A Multi-Layered Energy Efficient Approach for Performance Aware Internet of Ocean Things

Over seventy-one percent of the Earth's surface is covered by oceans, a vast body of water that is sometimes divided up into smaller bodies of water called seas. Research in this area is focusing on the significant advantages of an undersea or ocean-based environment, which is impacted by the ocean's temperature. Freshwater lakes and rivers cover less than 1% of the Earth's surface. So, ecosystems are damaged and modern technology must be developed so that climate and other elements may be regulated. The Internet of Underwater Things is cutting-edge technology in the ocean and underwater environments (IoUT). It's a network of underwater items that can be used to monitor large, undiscovered aquatic areas with a real-time Internet connection. There are several uses for IoUT, including in the military, strategic fields, climate research, and thermal pollution monitoring. With the use of the World Wide Web, Ocean of Things (OoT) devices may communicate in real-time using this technology. An IoUT is one of the most important IoT areas where a dynamic network is built and many advantages are reaped from the process. Using a multi-dimensional method for cumulative performance in the marine and undersea objects area, this research publication presents use examples for IoUT. Many significant applications and real-world domains are demonstrated to interface with crucial IoUT components. The proposed approach is making use of a multi-hop and metaheuristic-based approach for the effectiveness and overall performance of IoUT. As there are so many challenges in IoUT including frequent battery reduction and energy issues, there is a need to integrate the multi-layered approach. The presented approach is effective in terms of fewer packet losses, high throughput, and minimum energy consumption.

Mental Workload Artificial Intelligence Assessment of Pilots’ EEG Based on Multi-Dimensional Data Fusion and LSTM with Attention Mechanism Model

EEG has been proved to be an effective tool for researchers’ cognition and mental workload by detecting the changes of brain activity potential. The mental workload of pilots in aviation flight is closely related to the characteristics of flight tasks. The previous methods have problems such as lack of objectivity, low EEG analysis ability and lack of real-time analysis ability. In order to solve these problems, this paper proposes a multi-dimensional data fusion brain workload calculation method based on flight effect evaluation, which integrates vision, operational behavior and visual gaze, and classifies and analyzes them in combination with EEG data. This method evaluates the mental workload of pilots from three aspects: visual gaze behavior, control behavior and flight effect in the simulated flight experimental environment, and realizes a more objective mental workload analysis. Then, the synchronously collected EEG data are segmented and sampled to form a dataset, and an LSTM neural network model integrating attention mechanism is established, in which the attention mechanism is used to improve the feature processing ability of the network model for the classification of complex EEG data. After machine learning training, the final model can achieve 94% detection accuracy for 2-s EEG data, and has the ability of real-time analysis in the application environment. Compared with the previous similar LSTM model, the accuracy is improved by 6%, which also shows the effectiveness of the model.

Synthesis of Difference Patterns for 3-D Conformal Beam-Scanning Arrays With Asymmetric Radiation Aperture

Due to the asymmetry of the elements’ position distribution and polarization direction, it is a challenge to synthesize difference patterns with large slope and symmetric main-lobe on 3-D conformal beam-scanning arrays with asymmetric radiation aperture. In this article, an approach to synthesize the optimal difference patterns for 3-D conformal arrays with asymmetric radiation aperture is proposed. To determine the limitation region of the main-lobe and sidelobe for a 3-D conformal array, an initial difference pattern is generated by the joint multidimensional vector clustering method. Then, an auxiliary phase function is introduced to convert the nonconvex constraint of the symmetry of the difference beam into a convex one. Finally, an iterative convex optimization is applied to obtain the optimal difference pattern. The optimization progress is finished by extracting the vector field patterns of a simulated antenna element. Two design examples employing hemispherical conformal array (HCA) and conical conformal array (CCA) with asymmetric radiation aperture are implemented to demonstrate the effectiveness of the proposed method. Difference patterns with −20 dB sidelobe level (SLL) and large slope are obtained in both numerical and simulated results of the two design examples.

Методика оценки влияния эксплуатационных параметров на работу турбопитательного насоса

Background. Turbine feed pumps (TFP) are one of the main consumers of thermal energy for in-house needs of thermal power plants and nuclear power plants. Currently, much attention is paid to improve the efficiency of operation of turbine feed pumps. Thus, it is necessary to assess the impact of operational parameters. The problem is that it is necessary to process a large array of data. The solution to the problem is the development of a methodology that allows quantitative and qualitative assessment of operational parameters. Materials and methods. As initial data, the authors have used the data of 20 thermodynamic and hydraulic parameters recorded during the industrial operation of the TFP equipment and the equations of thermal balances to find the key performance indicators of the TFP. A complex of multidimensional statistical methods is used for data processing, i.e cluster and factor analysis in the Statistica software package. Results. A methodology to assess the impact of parameters on the operation efficiency of the TFP has been developed. The cluster analysis method is used to diagnose and classify the parameters under study. By the method of factor analysis, several parameters have been identified: feed water flow in the pipeline behind the TFP, differential pressure of water of the TFP, condensate flow in the pipeline behind the TFP condensate pumps, pressure drop of circulating water before and after the TFP condenser, condensate temperature of the drive turbine. The analysis of the results has showed that the identified parameters are the main components of the useful power of the turbine feed pump and the criteria for the efficiency of the TFP. Conclusions. The proposed methodology makes it possible to carry out a quantitative and qualitative assessment of operational parameters, solve the problem of processing large amounts of information, reduce the time and complexity of calculations. The results obtained are based on the developed methodology and can be used to design a statistical model using neural network technologies. This methodology can be chosen to evaluate the operation of technological systems and subsystems at thermal power plants and nuclear power plants.

A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

The online ideological as well as the political teaching resource management system structure is established in the view of information management in colleges and universities. Furthermore, the online ideological as well as the political teaching information level is improved by combining the optimized design of resource recommendation model. In this paper, an online ideological as well as political teaching resource adaptive recommendation system and algorithm, which is designed on deep reinforcement learning, is suggested. The cost relationship model between online ideological as well as the political teaching resources and learning profitability is constructed. Similarly, the multidimensional constraint index parameter analysis method is adopted, and the adaptive matching model of online ideological as well as the political teaching resources is established. According to online ideological as well as the political teaching norms, combined with the analysis of high-quality educational resources of audience groups, the dynamic evaluation of online ideological as well as the political teaching resources and the adaptive matching model of interest preferences are established. Finally, the deep reinforcement learning method is adopted. By analyzing the characteristics of the resource structure model of online ideological as well as the political teaching resources, through benefit evaluation, resource supply and demand balance management analysis and balanced game control, the online ideological as well as the political teaching resources management system can be improved and self-adaptive recommended. The simulation outcomes indicate that this approach has noble adaptability and high correctness in recommending online ideological as well as the political teaching resources.

The exploration in the size of scientific collaboration team using kernel density estimation

PurposeScientific collaboration is becoming a common pattern in the social organization of knowledge production. The paper tries to figure out the relationship between scientific collaboration team size and scientific output.Design/methodology/approachBased on ESI database from year 2009–2019, the paper describes changes of collaboration team size from one author to more than 10 authors in 22 disciplines. Kernel density estimation and multidimensional kernel density estimation method are used to calculate optimal collaboration team size and appropriate collaboration team size in 22 disciplines. As bandwidth is one of the major issues in construction of kernel density estimation, the paper uses five different algorithms to calculate bandwidth. The method with the lowest mean absolute percentage error is chosen. Robustness test is conducted based on different sets of data.FindingsThe results show that scientific collaboration becomes more widely and deeply. As time goes by, collaboration team size is becoming larger and larger. Natural science disciplines have larger collaboration team size and faster growth rate than social science disciplines. Considering both qualitative and quantitative measures, the paper proves the universality of optimal and appropriate scientific collaboration team size among 22 disciplines and calculates the specific number.Originality/valueThe paper tries to investigate the law of scientific collaboration team size variation and provide a full picture of evolution of collaboration team size among 22 disciplines in 10 years. The paper first applies distribution method to figure out the relationship between scientific collaboration team size and scientific output and provides optimal collaboration team size and appropriate collaboration team size.

What draws voters to brandidates and why? – Political orientation, personal satisfaction, and societal values on presidential candidates’ brand personality

PurposeThe purpose of this study is to determine the impact of voters’ personal and societal values on presidential candidate brand personality preference. In addition, the research examines which brand personalities are deemed most and least important. This research meets the growing demand to further understand how voters develop preferences for brandidates.Design/methodology/approachVoters ranked which presidential brand personalities they deemed most important in a candidate as well as which of the two major candidates they most associated with that trait. Data were collected weeks in advance of the 2020 presidential election from a national online panel representing a balanced mix of voters by party affiliation.FindingsThe results indicate that life satisfaction, political orientation and postmaterialism are significant and provide adequate explanatory power in understanding which brand personality traits are associated with a presidential candidate. Also, using an importance-performance matrix, the authors find which candidate is most identified with various brand personality traits and how important those traits are to voters.Research limitations/implicationsUsing the importance-performance matrix for assessing brand/candidate personality preference by consumers/voters provides researchers with a multidimensional method for analyzing how various dimensions influence selection preference. The explanatory power of the independent variables, i.e. political orientation, comparative life satisfaction and societal values, is very low when regressed against personality attributes in general (not assigned to a candidate); however, they provide meaningful results when regressed against personality attributes when assigned to candidates. Understanding the importance of general brand personality attributes is not as important as understanding their importance when associated with a specific brand.Practical implicationsThe importance-performance matrix for brand/candidate personality presented in this research clearly indicated and predicted voter preference for the 2020 Presidential election; thus, this tool can be effectively used by political marketers in future elections. Political orientation so strongly influences voter perception of specific candidate brand personality dimensions that they view their preferred candidate to be universally superior to other candidates. Political marketers can appeal to voters based on their political orientation to strengthen the relationship between candidates and voters.Originality/valueThis research investigates how personal and societal values impact voters’ preference for brand personality traits in a presidential candidate. Voter preference for presidential brand personality traits is assessed generically, i.e. not associated with a particular candidate, as well as when they are linked to a specific candidate, i.e. Biden and Trump.