Hierarchical Modeling Analyses Research Articles

A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences

We introduce a novel Bayesian hierarchical model for consumer preference analysis, addressing two significant challenges in this domain. First, it accommodates preference heterogeneity at both individual and segment levels. This enables actionable insights for targeting and pricing decisions while quantifying uncertainty. Second, it incorporates probabilistic value-based ranking to handle inconsistent and sparse preference data. This way, it mitigates the impact of cognitive biases and alleviates uncertainty in estimates. The proposed method performs robust inference of consumers’ preferences through hierarchical priors, allowing for flexible parameter learning and borrowing statistical strength from well-informed individuals. We demonstrate its practical usefulness by analyzing the real preferences of almost one hundred consumers considering mobile phone contracts. We also report the results of an extensive experimental study. The proposed method outperforms its counterpart, executing an independent estimation and the state-of-the-art approaches regarding predictive accuracy and preference similarity within identified customer groups. The performance improvements are more pronounced with larger sample sizes, smaller sets of items, and in contexts with reduced heterogeneity and increased consistency among consumers.

Measuring sustainability consciousness in Italy

AbstractSustainability can be conceived as an ideal state in which society, environment and economy can thrive without harming each other in the present, future and global space. In this paper, we validate the Italian version of the sustainability consciousness questionnaire developed by a Swedish research group. The psychometric construct aims to measure individual sustainability consciousness through a combination of knowingness, attitudes and behaviour. The validation process is based on the hierarchical confirmatory factor analysis model and includes both the long and short forms of the questionnaire proposed in the original study. Specifically, we propose two alternative approaches to validate the short form, one based on the same subset of items as the original proposal and another based on a data-driven strategy that resulted in a different subset of items. Once validated on a national level, the sustainability consciousness questionnaire becomes a valuable tool for researchers and policymakers to assess the sustainability consciousness levels within a national or local population regarding this crucial issue, enabling the design of more precisely targeted policies.

Dielectric spectroscopy with machine learning integrated approach to analysis the influence of N:P ratio on algae growth modeling for forecasting

The task of monitoring algal biomass is a significant challenge in contemporary environmental conservation. A novel technique called dielectric spectroscopy with machine learning integration is an intelligent approach to address this challenge. This work optimized a machine learning supervised PCA, Hierarchical cluster analysis & linear regression model -strengthen the real-time dielectric spectroscopy approach to study the influence of N:P ratios on a model algae Desmodesmus sp. growth for forecasting. As a primary feature, different N:P concentrations (1:1, 2:1, 3:1, 1:2, and 1:3) were utilized to characterize the relationship between dielectric (S11(dB) & ε'') and conventional UV–vis spectroscopy properties (OD686 & Chl-a). The dielectric measurements of S11(dB) & ε'' are consistent with the OD686 & Chl-a value. Highest Desmodesmus sp. OD686 0.87, 0.76 recorded at N:P 3:1, 2:1 ratio respectively. The machine learning based PCA, Hierarchical cluster analysis and linear regression model were applied and found at 0–8 days incubation Desmodesmus sp. growth biomass negatively corelated with S11(dB) & ε“ and positively correlatedly with OD686 & Chl-a respectively, with R2 values > 0.95. While for the 0&8days measured ε”& S11(dB) were −6.169&-0.4765; −7.956 &-0.6775 respectively, the linear regression model was applied to predicted OD686 for 0&8 days was 0.299, 0.234; 0.817, 0.716 respectively, at the ratios N:P (3:1) and the RMES were 0.0047 & 0.0319 respectively. This analysis revealed that the machine learning based integrated approach has high accuracy. Thus, Dielectric spectroscopy and machine learning-integrated method could strengthen to the real-time monitoring of algal biomass or predicting to control the N:P ratios to protecting aquatic ecosystem.

Characteristics and Optimization of University English Translation from a Cross-cultural Perspective-Based on Functional Translation Theory

Abstract Drawing on the theoretical framework of functional translation, this study presents a multifaceted exploration of university English translation through functional, constructive, evolutionary, and cultural lenses. Positioned within a cross-cultural context, the paper categorizes university English translation into three primary attributes: the frontier nature of translation, linguistic and cultural divergences, and the professionalism required in translation activities. Utilizing Super Decision software, these attributes serve as input variables to construct a Network Hierarchical Analysis model. This model is further integrated with the Interpretive Structural Modeling (ISM) recursive structure to assess the influence of various factors on translation quality. The study employs a multidimensional approach to dissect the impact of cultural and professional variances on the quality of university English translation. The results reveal that 54 out of 65 cultural dimensions (83.08%) significantly affect translation quality. In the realm of professional characteristics, four out of six dimensions (66.67%) are found to have a considerable influence on translation quality. Based on these comprehensive findings, the paper proposes four strategic optimizations aimed at enhancing the quality of university English translation, thereby offering valuable insights and practical recommendations for the field.

Matrix model-based Analysis of e-commerce development and urban entrepreneurial activity

Abstract China’s e-commerce market size, the most active and best-developed component of the digital economy, has become an essential driver of economic growth and urban entrepreneurial activity. This paper analyzes e-commerce development and urban entrepreneurial activity through BCG matrix. It establishes the framework of AHP-SWOT hierarchical analysis model combining with SWOT matrix model and AHP hierarchical analysis. In the development of e-commerce, the national e-commerce turnover in 2020 is 37.21 trillion yuan, with a year-on-year growth rate of 57.60% in 2014. Meanwhile, in 2020, the number of e-commerce employees reaches 60 million, which is 1.23 times of the number in 2014. Due to the rapid development of the economy and e-commerce, resulting in urban entrepreneurial activity is also gradually rising, the number of private enterprises in 2012 grew from 5254870 to 18921928 in 2020, an increase of up to 260%. 2020, the number of new market entities is 27,495,700, the country’s entrepreneurial activity is a year-on-year upward trend. Through the development of e-commerce and urban entrepreneurial activity, the quality of employment and entrepreneurship in society is promoted to a higher level.

Optimization of Food Distribution System Based on Dynamic Planning Model

Based on the data of per capita annual income, food production per hectare, and per capita consumption of consumers in different countries, this paper first establishes a dynamic planning model, starting from three indicators, namely, regional transportation time, purchasing capacity and actual demand, and using MATLAB, establishes a visual image to derive the changes of satisfaction, profitability, environment, and efficiency in the next ten years. In order to determine the priority of the four indicators, a hierarchical analysis model is established on the basis of the dynamic planning model to derive the calculated weights of each indicator. In order to verify the adaptability of the model, the model is applied to the developed country Britain and the developing country China, respectively, and the dynamic planning model is confirmed again according to the information returned by the visualization image, which illustrates the scalability of the model to the food distribution system in different countries and regions.

Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data

AbstractUsing turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals before starting a lane change. In this study, realistic driving data are analyzed to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. Turn signal usage is examined and factors that influence it are identified by employing Bayesian hierarchical modelling. The study found that drivers used their turn signal before changing lanes in 60% of cases, after starting the lane change in 33% of cases, and did not use it at all in 7% of cases. The Bayesian hierarchical modelling results indicate that various factors, such as the speed and direction of lane changes and the presence of surrounding vehicles, influence the usage of turn signals. The study concludes that understanding the factors affecting turn signal usage is crucial for improving traffic safety in current and future mixed traffic with autonomous vehicles. The study discusses the implications of findings concerning increasing turn signal compliance through general policy‐making, improving existing in‐vehicle technologies and including turn signal usage in Pay‐As‐You‐Drive insurances.

Human ratings take time: A hierarchical facets model for the joint analysis of ratings and rating times.

Performance assessments increasingly utilize onscreen or internet-based technology to collect human ratings. One of the benefits of onscreen ratings is the automatic recording of rating times along with the ratings. Considering rating times as an additional data source can provide a more detailed picture of the rating process and improve the psychometric quality of the assessment outcomes. However, currently available models for analyzing performance assessments do not incorporate rating times. The present research aims to fill this gap and advance a joint modeling approach, the "hierarchical facets model for ratings and rating times" (HFM-RT). The model includes two examinee parameters (ability and time intensity) and three rater parameters (severity, centrality, and speed). The HFM-RT successfully recovered examinee and rater parameters in a simulation study and yielded superior reliability indices. A real-data analysis of English essay ratings collected in a high-stakes assessment context revealed that raters exhibited considerably different speed measures, spent more time on high-quality than low-quality essays, and tended to rate essays faster with increasing severity. However, due to the significant heterogeneity of examinees' writing proficiency, the improvement in the assessment's reliability using the HFM-RT was not salient in the real-data example. This discussion focuses on the advantages of accounting for rating times as a source of information in rating quality studies and highlights perspectives from the HFM-RT for future research on rater cognition.

Effect of Demographic Factors, Radiation Knowledge Level, Radiation Awareness on Radiation Benefit by Hierarchical Regression Analysis Model

Effect of Demographic Factors, Radiation Knowledge Level, Radiation Awareness on Radiation Benefit by Hierarchical Regression Analysis Model

Research on the cultivation path of craftsmanship in university education based on fuzzy hierarchical analysis

Abstract This paper firstly explores the evaluation method of the level of cultivation of craftsmanship in colleges and universities and decomposes the problem to be analyzed into each component element that affects its goal by using the fuzzy hierarchical evaluation method. The hierarchical structure analysis model is established, and the upper influence factors of each layer are used as the basic criteria to compare each influence factor of the program layer and the criterion layer with each other, and the fuzzy consistency matrix is constructed to determine the relative weights of each divided influence factor. Then, the current situation of the level of cultivation of craftsmanship in college education and the problems of the cultivation mechanism of craftsmanship in colleges and universities were analyzed, and suggestions for improvement were put forward. In terms of the existing problems, 70% of them think that the atmosphere of attaching importance to craftsmanship spirit is insufficient, 80% of them think that the teaching method of craftsmanship spirit is in a single way, and 86% of them think that the excavation of craftsmanship spirit is not deep. In terms of the recognition of craftsmanship spirit, “respecting teachers and respecting morality”, “being willing to devote”, “being rigorous and meticulous”, “inheriting tradition “ and “innovation and creation” are all around 60%, while “excellence”, “love for work”, and “endurance of loneliness” are all around 60%. The approval rate of the artisan spirit of “enduring loneliness” is between 70% and 75%. The research of this paper is of great significance to the cultivation of craftsmanship in universities.

Exploring the predictors of teachers’ teaching autonomy: A three-level international study

Concerning the critical function of teachers' teaching autonomy in instructional practice and professional development, this study explores the relative importance of three predictive mechanisms: a. professionalism of teachers, b. complexity of teaching practice, and c. culture of individualism. Using the sample of 118,347 teachers from 8262 schools in 27 countries derived from the 2018 Teaching and Learning International Survey (TALIS), we applied a three-level hierarchical linear model (HLM) for analysis. We found that teachers’ teaching autonomy was mainly predicted by the professionalism of teachers and the culture of individualism rather than the complexity of teaching practice. Implications for individual teachers, school leaders, and policymakers are discussed.

Joint spatial modeling of the risks of co-circulating mosquito-borne diseases in Ceará, Brazil

Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.

Natural element hierarchical models for static and free vibration analysis of cylindrical panels

Most of studies on the hierarchical models were restricted to beam- and plate-like structures using the finite element method. In this context, this study intends to present the hierarchical models for cylindrical panels and investigate their characteristics using 2-D natural element method (NEM). The displacement field of cylindrical panel is decomposed into the triple-vectored in-plane functions and the assumed thickness polynomials, and the hierarchical models are defined by sequentially increasing the maximum orders of thickness polynomials from (1,1,0). The triple-vectored in-plane functions are approximated by applying 2-D NEM to the unfolded rectangular plane of curved mid-surface of cylindrical panels, for which the stiffness matrices corresponding to the membrane and transverse strains and stresses are calculated using the selectively reduced integration technique to avoid shear-membrane locking. The hierarchical models are demonstrated and validated from the comparative numerical experiments of bending and free vibration of cylindrical panels. The present hierarchical models provide the central deflections and the natural frequencies which are in good agreement with the reference solutions. In addition, the characteristics of hierarchical models such as the limit property and the modeling and approximation errors are investigated with respect to the model level and the width-thickness ratio and the grid density. It is found that the hierarchical models show the locking-free robust and spectral variation in the central deflection and natural frequencies.

Delayed Mid-Sleep Time Associated With Weight Gain While Controlling for Eating Behaviors and Attention Deficit Hyperactivity Disorder Symptoms During the COVID-19 Pandemic.

Society's sleep-wake cycle and eating behaviors have altered and are considered the psychological outcomes of the coronavirus disease-2019 (COVID-19) pandemic. Our aim was to examine the relationship between sleep-wake rhythms, eating behaviors (dieting, oral control, and bulimic behaviors), and attention deficit hyperactivity disorder (ADHD) symptoms with weight gain during the COVID-19 pandemic. The participants were 578 female university students divided into three groups based on weight change during COVID-19 who lost weight, whose weight did not change (nWC), and who gained weight (WG). The participants' information about weight change in the last year and responses to the Pittsburg Sleep Quality Index, Eating Attitudes Test, Adult ADHD Severity Rating Scale, and Wender Utah Rating Scale were collected via an online survey from January 8, 2021 to January 11, 2021. The sleep-wake phase was more delayed in WGs than in the other two groups. The bulimic behavior score was higher and the oral control behavior score was lower in the WG group than in the nWC group. A hierarchical regression analysis model, in which weight change scores were dependent variables, showed that mid-sleep time in second step (β=4.71, t=2.18, p=0.03), and oral control (β=-0.11, t=-3.24, p=0.001)/bulimic behaviors (β=0.20, t=3.20, p=0.001) in third step were associated with weight change after controlling for both current and childhood ADHD symptoms. Chronotherapeutic approaches that regulate sleep-wake rhythm may facilitate weight control of individuals during stressful periods, such as the COVID-19 outbreak.

Retracted: Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

Retracted: Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

Retracted: Construction and Analysis of Performance Evaluation Index System for Chinese Small and Medium-Sized Enterprises Based on Fuzzy Hierarchical Analysis Model.

[This retracts the article DOI: 10.1155/2022/1230786.].

Leveraging hierarchical language models for aspect-based sentiment analysis on financial data

Every day millions of news articles and (micro)blogs that contain financial information are posted online. These documents often include insightful financial aspects with associated sentiments. In this paper, we predict financial aspect classes and their corresponding polarities (sentiment) within sentences. We use data from the Financial Question & Answering (FiQA) challenge, more precisely the aspect-based financial sentiment analysis task. We incorporate the hierarchical structure of the data by using the parent aspect class predictions to improve the child aspect class prediction (two-step model). Furthermore, we incorporate model output from the child aspect class prediction when predicting the polarity. We improve the F1 score by 7.6% using the two-step model for aspect classification over direct aspect classification in the test set. Furthermore, we improve the state-of-the-art test F1 score of the original aspect classification challenge from 0.46 to 0.70. The model that incorporates output from the child aspect classification performs up to par in polarity classification with our plain RoBERTa model. In addition, our plain RoBERTa model outperforms all the state-of-the-art models, lowering the MSE score by at least 28% and 33% for the cross-validation set and the test set, respectively.

Effect evaluation of ideological and political guidance in university party construction based on hierarchical analysis model

Abstract To further explore the evaluation model of the quality of college students working in vocational colleges, this paper proposes to establish an evaluation index system for the quality of college students working in vocational colleges according to the four main factors of the domestic vocational college, which is based on the analysis of the quality. the work of the party building, the quality and responsibility of the party members, and the mass examination; In the process of evaluating the quality of college students’ dormitory building, various useful and useful information were determined, and the evaluation model was established by using the analytic hierarchy process, and the weight coefficients of various influential factors were determined by the combined method. First of all, according to the characteristics and requirements of the student’s institution, combined with the system of employment management widely used in China, combined with the practice of students’ institution, thoroughly analyzed various factors which affect the quality of the student’s institution building, and establish a more effective and objective evaluation index system. Secondly, using the analytic hierarchy process to scientifically classify the indicators, and determine the evaluation factor set and evaluation criteria, establish a hierarchical structure model. Finally, combined with the hierarchical structure model, the relative weight of each evaluation factor is given using the principle of fuzzy mathematics.

Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation

The state of in situ stress is a crucial parameter in subsurface engineering, especially for critical projects like nuclear waste repository. As one of the two ISRM suggested methods, the overcoring (OC) method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test. However, such customary independent analysis of individual OC tests, known as no pooling, is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method. To address this problem, a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests, which are usually available in OC measurement campaigns. Hence, this paper presents a Bayesian partial pooling (hierarchical) model for combined analysis of adjacent OC tests. We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden. The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests, and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling, particularly for those unreliable no pooling stress estimates. A further model comparison shows that the partial pooling model also gives better predictive performance, and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

Assessment of Seismic Vulnerability in Urban and Rural Health Service Centers of Hamadan Province Using Geographic Information Systems

Background: Maintaining the performance of healthcare centers during an earthquake is essential. Evaluating the seismic vulnerability of these centers is a priority. This study aims to evaluate seismic vulnerability using geographic information systems (GIS) in urban and rural health service centers of Hamadan Province, Iran, in 2021. Materials and Methods: This is a descriptive, analytical cross-sectional study that was conducted in comprehensive health centers. The data of this study were collected in the form of a questionnaire and a map and analyzed using the hierarchical analysis model. In the study, 10 criteria, such as the type of materials, the age of the building, the quality of the building, the number of floors, the area of ​​the centers, as well as the distance from the existing fault lines, and the proximity to the hazardous centers, are defined using a valuation questionnaire, using the chain analysis method, weighting and prioritizing, and using GIS, vulnerability maps of comprehensive urban and rural health centers of Hamadan province were obtained. Results: Among the indices, the distance from the fault index with a weight of 0.328 has the highest and the area distance with a weight of 0.17 has the lowest final weight. The vulnerability distribution of the centers included 46.99% very low vulnerability, 7.23% low vulnerability, 5.42% moderate vulnerability, 20.48% high vulnerability, and 19.88% very high vulnerability. Conclusion: Considering that many urban and rural health service centers are vulnerable to seismic hazards, it is necessary to strengthen vulnerable centers against earthquakes.