Hierarchical Modeling Method Research Articles

Hierarchical Holographic Modeling of Network Ideology Risks

The rapid evolution of computer network technology and the proliferation of social information have elevated the role of computer networks in various domains. However, this progress has also introduced new risks to network ideology security within the big data environment. Using a hierarchical holographic modeling method, this study identifies three key risks—subject-related, information-related, and environmental risks—and constructs a comprehensive three-level index framework for evaluating network ideology risks. By integrating network social governance with the complexities of the big data era, the study proposes an optimized pathway for network social governance, enriching traditional research approaches. The introduction of the LR-NSRPM method reduces CPU and RAM utilization by 21.65% compared to existing methodologies. Additionally, the study outlines observable risk indicators and high-risk elements within network ideology, providing valuable insights for managing network ideology risks in the big data environment.

Data-driven hierarchical collaborative optimization method with multi-fidelity modeling for aerodynamic optimization

The aerodynamic optimization of aircraft based on evolutionary algorithms generally requires thousands or even tens of thousands of iterations. Moreover, it faces challenges such as high-dimensional design variables and high computational costs, leading to low efficiency in algorithm search and poor optimization results. In order to enhance the efficiency of aerodynamic optimization, a data-driven collaborative optimization and multi-fidelity hierarchical modeling method is proposed. Firstly, a Clustering-Enhanced Teaching-Learning-Based Optimization (CETLBO) algorithm is introduced to improve its exploration and exploitation capabilities. By employing a collaborative optimization strategy, a divide-and-conquer paradigm is utilized to efficiently solve high-dimensional, nonlinear, and complex engineering optimization problems. Secondly, a two-stage hierarchical aerodynamic optimization framework is designed to reduce computational costs by integrating data from different fidelity levels. Stage 1 adopts a data-driven approximate optimization method to provide prior knowledge for the high-fidelity optimization of stage 2. Finally, the proposed method is applied to the optimization design of supersonic wing shapes. Compared to traditional optimization design systems, it demonstrates high efficiency in high-dimensional aerodynamic optimization problems, yielding better optimization results.

Rolling contact fatigue analysis of the soft zone for the main bearing in a tunnel boring machine

A hierarchical finite element modeling method is proposed to study the problem of fatigue spalling easily due to low hardness in the soft zone for the main bearing of a tunnel boring machine. Quasi-static analyses of the main bearing are performed based on the global symmetric model and verified by relative displacement tests of the rings. The invariant contact pressure is replaced by the rolling contact of the rollers in the local sub-model. The contact load is used as an input to solve the stress field, and then the effects of soft zone and hardened depth on the contact characteristics and fatigue lifetime of the sub-model are investigated. The results show there are stress peaks at the junction of the hardened and soft zones. The effect of hardened depth on the stress distribution and fatigue lifetime is relatively small. However, the optimum hardened depth is influenced by the contact load and the permissible stress of the material. The minimum lifetime of the raceway during rolling occurs in the soft zone. This paper achieves a fast and effective rolling contact fatigue analysis of the main bearing, which provides a theoretical basis for optimizing the main bearing design.

Bayesian hierarchical modeling in interim futility analysis for two parallel clinical trials.

Incorporating interim analysis into a trial design is gaining popularity in the field of confirmatory clinical trials, where two studies may be conducted in parallel (ie, twin studies) in order to provide substantial evidence per the requirement of FDA guidance. Interim futility analysis provides a chance to check for the "disaster" scenario when the treatment has a high probability to be not more efficacious than the control. Therefore, it is an efficient tool to mitigate risk of running a complete and expansive trial under such scenario. There is no agreement among trial designers that interim analysis should be based on individual study data or pooled data under the twin study scenario. In fact, it is a dilemma for most scientists when specifying the interim analysis strategy at the design stage as the true treatment effects of the twin studies are unknown no matter how similar they are intended to be. To address the issue, we developed a Bayesian hierarchical modeling method to allow dynamic data borrowing between twin studies and demonstrated a favorable characteristic of the new method over the separate and pooled analyses. We evaluated a wide spectrum of the heterogeneity hyperparameters and visualized its critical impact on the Bayesian model's characteristic. Based on the evaluation, we made a suggestion on the heterogeneity hyperparameter selection independent of any a priori knowledge. We also applied our method to a case study where predictive powers of different methods are compared.

Development of a Predictive Statistical Pharmacological Model for Local Anesthetic Agent Effects with Bayesian Hierarchical Model Parameter Estimation.

As an alternative to animal use, computer simulations are useful for predicting pharmacokinetics and cardiovascular activities. For this purpose, we constructed a statistical model to simulate the effects of local anesthetic agents. To train the model, animal experiments were performed on 6-week-old male Hartley guinea pigs. Firstly, the guinea pigs' backs were shaved, then local anesthetic agents were subcutaneously injected, with subsequent stimulation of the anesthetized site with a needle six times at regular intervals. The number of reactions (score value) was counted. In this statistical model, the probability of reacting to needle stimulation was calculated using the elapsed time, type of local anesthetic agent, and presence or absence of adrenaline. Score values were assumed to follow a binomial distribution at the calculated probability. Parameters were estimated using the Bayesian hierarchical model and Hamiltonian Monte Carlo method. The predicted curves using the estimated parameters fitted well the observed animal values. When score values were predicted using randomly generated parameters, the median of duration was similar between animal experiments and simulations (Procaine: 55 min vs. 50 min, Lidocaine: both 60 min, and Mepivacaine: both 85 min). This approach effectively modeled the effects of local anesthetic agents. It is possible to create the simulator using the parameter values estimated in this study.

Understanding polysubstance use at the daily and event levels: protocol for a mixed-methods qualitative and ecological momentary assessment study in a community-based sample of people who use illicit drugs in Oakland, California, USA

IntroductionPolysubstance use is extremely common among people who use illicit opioids in the USA. It is associated with poor substance use treatment outcomes, infectious disease risk and alarming rates of...

A hierarchical model-based method for wafer level virtual metrology under process information deficiency

Online inspection is one of the most critical processes of quality control in semiconductor manufacturing. The physical inspection methods for wafers are time-consuming and unable to achieve wafer level metrology. In order to improve production efficiency and expand inspection coverage, virtual metrology (VM) methods have recently received widespread attention; they utilize process parameters to estimate wafer metrology results. However, due to process drift and other reasons, the process information contained in real-time signal data (RTS data) used for VM modeling in industrial production is insufficient. This work proposed a hierarchical modeling method for machine learning-based virtual wafer metrology, leveraging RTS and post-process quality characteristics. The hierarchical model consists of an multiway principle analysis (MPCA) sub-model for RTS feature extracting and two separate long short-term memory (LSTM) networks for wafer-to-wafer dynamics in RTS and quality characteristics, respectively. A case study on the thickness VM of chemical vapor deposition thin film is conducted, and the proposed method has achieved better results than other methods in comparison.

Layout Representation Learning with Spatial and Structural Hierarchies

We present a novel hierarchical modeling method for layout representation learning, the core of design documents (e.g., user interface, poster, template). Existing works on layout representation often ignore element hierarchies, which is an important facet of layouts, and mainly rely on the spatial bounding boxes for feature extraction. This paper proposes a Spatial-Structural Hierarchical Auto-Encoder (SSH-AE) that learns hierarchical representation by treating a hierarchically annotated layout as a tree format. On the one side, we model SSH-AE from both spatial (semantic views) and structural (organization and relationships) perspectives, which are two complementary aspects to represent a layout. On the other side, the semantic/geometric properties are associated at multiple resolutions/granularities, naturally handling complex layouts. Our learned representations are used for effective layout search from both spatial and structural similarity perspectives. We also newly involve the tree-edit distance (TED) as an evaluation metric to construct a comprehensive evaluation protocol for layout similarity assessment, which benefits a systematic and customized layout search. We further present a new dataset of POSTER layouts which we believe will be useful for future layout research. We show that our proposed SSH-AE outperforms the existing methods achieving state-of-the-art performance on two benchmark datasets. Code is available at github.com/yueb17/SSH-AE.

Formal Analysis of Reentrancy Vulnerabilities in Smart Contract Based on CPN

A smart contract is a special form of computer program that runs on a blockchain and provides a new way to implement financial and business transactions in a conflict-free and transparent environment. In blockchain systems such as Ethereum, smart contracts can handle and autonomously transfer assets of considerable value to other parties. Hence, it is particularly important to ensure that smart contracts function as intended since bugs or vulnerabilities may lead, and indeed have led, to substantial economic losses and erosion of trust for blockchain. While a number of approaches and tools have been developed to find vulnerabilities, formal methods present the highest level of confidence in the security of smart contracts. In this paper, we propose a formal solution to model a smart contract based on colored Petri nets (CPNs). Herein, we focus on the most common type of security bugs in smart contract, i.e., reentrancy bugs, which led to a serious financial loss of around USD 34 million for the Cream Finance project in 2021. We present a hierarchical CPN modelling method to analyze potential security vulnerabilities at the contract’s source code level. Then, modeling analysis methods such as correlation matrix, state space report and state space graph generated via CPN Tools simulation are exploited for formal analysis of smart contracts. The example shows the full state space and wrong path in accordance with our expected results. Finally, the conclusion was verified on the Ethereum network based on the Remix platform.

Malignant melanoma dermoscopy image classification method based on multi‐modal medical features

AbstractSkin cancer is one of the deadliest cancers, and it has been widely developed worldwide since the last decade. Malignant melanoma is currently the most deadly skin cancer. If malignant melanoma is diagnosed at an early stage, the probability of patients being cured will be greatly improved. At present, most existing skin lesion image classification methods only use deep learning. However, the multi‐modal features of skin lesions in the medical domain are not well utilized and integrated. To reduce the classification error of the skin lesion images caused by the complexity and subjectivity of visual interpretation, a malignant melanoma dermoscopy image classification method based on multi‐modal medical features is proposed in this paper which is inspired by the fuzzy decision‐making process of doctors. It can reduce the subjective difference in the image classification process and assist dermatologists to analyze the skin lesion area. Firstly, the feature detection method based on the extension theory can effectively quantify the difference between different colour features. Then, an interpretable segmentation edge of the skin lesion is established by using the neutrosophic theory which can convert the image into the neutrosophic space. The edge of the skin lesion is captured by applying the Hierarchical Gaussian Mixture Model (HGMM) method. Next, the edge sequence is established by segmenting the edge, and the contour regularity, symmetry, and uniformity of the edge of the skin lesion are analyzed. Finally, the extracted multi‐feature sets are used for dermoscopy image classification. Experiments are carried out on real datasets, and the classification accuracy of four kernel functions is verified. The experimental results show that the authors’ method can effectively improve the classification accuracy of benign dermoscopy images and malignant dermoscopy images.

A posteriori boundary layer detection and resolution in hpq-adaptive finite element methods for 3D-based hierarchical plate and shell models

A posteriori boundary layer detection and resolution in hpq-adaptive finite element methods for 3D-based hierarchical plate and shell models

Research on Benefit Assessment of Multi-scenario Integrated Energy System

With the rapid development of economy, China’s energy is in the stage of rapid transformation, and the market demand for comprehensive energy service in the whole society is increasing. Therefore, China needs to vigorously promote the construction of a comprehensive energy system energy supply mode with new energy as the main body, so as to realize the reasonable allocation of resources and the high proportion of clean energy consumption. To effectively evaluate the quality of the system operating benefits, this paper with a variety of typical comprehensive energy system as the background, through the establishment of comprehensive energy system scenario operating benefit evaluation index system, build index based on hierarchical analysis method weight model and entropy method of fuzzy comprehensive evaluation model, evaluate the benefits of various types of comprehensive energy system.

Treatment-related adverse events associated with HER2-Targeted antibody-drug conjugates in clinical trials: a systematic review and meta-analysis.

Given the increasing use of HER2-targeted antibody-drug conjugates (ADCs) worldwide, the summary of toxicity incidence and profiles of these drugs is crucial to provide reference for clinical application. This meta-analysis aimed to estimate the mean incidences of treatment-related adverse events of HER2-targeted ADCs and to investigate the differences between different drugs and cancer types. We performed a systematic search of literature in PubMed, Embase, Web of Science, and Scopus databases from inception to February 1, 2022 and the last search was updated to August 1, 2022. Published prospective clinical trials on single-agent of the US Food and Drug Administration approved HER2-targeted ADCs with available count data regarding treatment-related adverse events were included. The primary outcomes were pooled incidences of treatment-related adverse events and differences between different drugs and cancer types. The data synthesis was performed using a Bayesian hierarchical modelling method and the protocol was registered in PROSPERO (CRD42022331627). A total of 39 studies (37 trials) involving 7688 patients across five cancer types were included in the final analysis. On pooling the data using Bayesian hierarchical modelling, the overall mean incidence of all-grade adverse events, high-grade adverse events, serious adverse events, and adverse events that resulted in drug discontinuation were 98.29% (95% CrI, 97.33%-99.07%, τ=1.49), 47.88% (95% CrI, 42.74%-53.17%, τ=0.37), 19.45% (95% CrI, 15.70%-23.67%, τ=0.55), and 10.52% (95% CrI, 8.03%-13.21%, τ=0.56), respectively. The most common all-grade adverse events were nausea (41.57%; 95% CrI, 40.46%-42.64%, τ=0.81), fatigue (35.86%; 95% CrI, 34.85%-36.96%, τ=0.65), and decreased appetite (28.84%; 95% CrI, 22.93%-36.87%, τ=0.76). The most common high-grade adverse events were thrombocytopenia (8.37%; 95% CrI, 7.75%-9.07%, τ=0.71), anaemia (6.49%; 95% CrI, 5.86%-7.11%, τ=1.06), and neutropenia (6.42%; 95% CrI, 5.76%-7.04%, τ=1.21). We found no difference in the mean incidences of adverse events among different cancer types, as well as different dosing regimens. However, trastuzumab deruxtecan (T-DXd) appeared to have higher mean incidences of adverse events compared with trastuzumab emtansine (T-DM1), especially for the higher dose of T-DXd (6.4mg/kg Q3W). The incidences of adverse events between two HER2-targeted ADCs were similar in different cancer types, but different HER2-targeted ADCs appeared to have different mean incidences of adverse events. The comprehensive summary of the adverse events of HER2-targeted ADCs is critical for clinicians caring for patients with cancer receiving HER2-targeted ADCs therapy. The National Natural Science Foundation of China (Grant No. 82073402) and Key R&D Plan of Hubei Province, China (No.2020BCA060) funded this study.

Why Good Employees Do Bad Things: The Link between Pro-Environmental Behavior and Workplace Deviance.

Despite the significance of pro-environmental behavior (PEB) in the workplace, most of the existing studies have neglected its negative work outcomes. Drawing upon moral licensing theory and cognitive dissonance theory, we construct a conceptual model of the influence mechanism of employees' PEB (i.e., public-sphere PEB, private-sphere PEB) on workplace deviance through psychological entitlement, and the moderating effect of rationalization of workplace deviance on the relationship between psychological entitlement and workplace deviance. Using two-stage survey data from 216 employees in China, we performed hierarchical regression analysis and structural equation modeling method to test our hypotheses. Our findings reveal that public-sphere PEB positively affects psychological entitlement, while private-sphere PEB negatively affects psychological entitlement. Psychological entitlement further positively affects workplace deviance. In addition, rationalization of workplace deviance strengthens the positive impact of psychological entitlement on workplace deviance. This study offers novel insights into the dark side of PEB literature by exploring the PEB-workplace deviance relationship. This study also contributes to managerial implications of how PEB leads to workplace deviance and how to address this issue.

Hierarchical Multiple-Point Geostatistical Modeling Method and Application Based on Braided River Reservoir Architecture

The Lower Shihezi Formation of the Daniudi gas field in the Ordos Basin is a typical reservoir of a braided river system in an alluvial plain, characterized by extensive braided river development, parallel development from the near source to the center of the basin, and frequent interweaving and cut stacking, as well as a complex deposition process that has seen frequent river channel changes. The braided river belt, braided channel, channel bar inside the river, and interlayer within the channel bar constitute a hierarchical and complicated architectural feature, which poses a great challenge to accurately characterize this type of reservoir for modeling. We proposed a hierarchical, level-by-level embedding, and progressive multiple-point geostatistical modeling strategy that is refined layer by layer according to a 5–3 level architectural unit hierarchy, with the modeling results of each level providing constraints for the next level modeling. The hierarchical geological model based on the combination of qualitative architectural anatomy and quantitative pre-architecture unit scale is critical in guiding the efficient development of the remaining gas in the braided river reservoir in Daniudi.

Control of parallelized bioreactors II: probabilistic quantification of carboxylic acid reductase activity for bioprocess optimization

Autonomously operated parallelized mL-scale bioreactors are considered the key to reduce bioprocess development cost and time. However, their application is often limited to products with very simple analytics. In this study, we investigated enhanced protein expression conditions of a carboxyl reductase from Nocardia otitidiscaviarum in E. coli. Cells were produced with exponential feeding in a L-scale bioreactor. After the desired cell density for protein expression was reached, the cells were automatically transferred to 48 mL-scale bioreactors operated by a liquid handling station where protein expression studies were conducted. During protein expression, the feed rate and the inducer concentration was varied. At the end of the protein expression phase, the enzymatic activity was estimated by performing automated whole-cell biotransformations in a deep-well-plate. The results were analyzed with hierarchical Bayesian modelling methods to account for the biomass growth during the biotransformation, biomass interference on the subsequent product assay, and to predict absolute and specific enzyme activities at optimal expression conditions. Lower feed rates seemed to be beneficial for high specific and absolute activities. At the optimal investigated expression conditions an activity of 1153 U mL^{-1} was estimated with a 90% credible interval of [992, 1321] U mL^{-1}. This is about 40-fold higher than the highest published data for the enzyme under investigation. With the proposed setup, 192 protein expression conditions were studied during four experimental runs with minimal manual workload, showing the reliability and potential of automated and digitalized bioreactor systems.

Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle.

Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model

The Fuzzy Hierarchical Model method can be used to predict the stage of heart disease. The use of the Fuzzy Hierarchical Model on complex problems is still not optimal because it is difficult to find a fuzzy set that provides a more optimal solution. This method can be improved by changing the membership function constraints using Genetic Algorithm to get better predictions. Tests carried out using 282 heart disease patient data resulted in a Root Mean Squared Error (RMSE) value of 0.55 using the best Genetic Algorithm parameters, including population size of 140, number of generations of 125, and a combination of cross-over rate and mutation rate of 0.4 and 0.6 whereas the RMSE value generated by the Fuzzy Hierarchical Model before being optimized by the Genetic Algorithm was 0.89. These results indicate an increase in the predictive value of the Fuzzy Hierarchical Model after being optimized using the Genetic Algorithm.

Dynamic characteristics and seismic response analysis of the bottle-shaped masonry ancient pagoda

To study the dynamic performance and seismic response of the bottle-shaped masonry ancient pagoda of Mafo Temple, the numerical model of the pagoda was established using the finite element software ABAQUS with a hierarchical modeling method. By inputting the El-Centro wave, Taft wave, and Lanzhou artificial wave, the seismic response of the pagoda was calculated, and the seismic damage mechanism was analyzed, as well as the distribution characteristics of the principal tensile stress and the seismic weak area. The results show that the stiffness and mass distribution of the pagoda are consistent in the height direction, and the deformation of the Sumeru throne and the top of the pagoda is significant under the earthquake. The pagoda bottom exceeds the limits of the material's tensile damage factor and principal tensile stress under moderate and large earthquakes. The stiffness of the top of the pagoda is severely degraded under large earthquakes. Therefore, the Sumeru throne and the top of the pagoda are the seismic weak parts of the ancient masonry pagoda. These results provide a basis for the seismic capacity evaluation of the bottle-shaped masonry pagodas.

MANAGERS’ ETHICS OF TAX EVASION: THE ROLES OF FAMILY, RELIGION, AND SOCIAL CONDITIONS

Tax income is one of the essential financial resources to maintain a nation’s development, as tax revenue promotes the advancement of social welfare and community affairs. However, tax evasion has been a persistent governmental and societal concern. In order to expand insights on tax evasion of managers, the authors used Emile Durkheim’s sociological theory of anomie to investigate the individual-level association between managers’ perceived family and religion importance and their attitude toward tax evasion. Additionally, the theory was employed to examine how country-level aspects moderate that individual-level relationship. The hierarchical linear modeling (HLM) method was utilized to analyze data of 3,475 managers from 47 countries obtained from the World Values Survey (WVS). Results indicated that the managers’ perceived family and religion importance were negatively related to their ethics of tax evasion. Moreover, the individual-level relationships were moderated by the country-level factors of poverty, good governance, political integration, and social integration. Most study findings supported Durkheim’s original propositions, whereas complementary arguments were offered to explain the results contradicting them. Since there are no existing studies on the influence of managers’ perceived family and religion importance on the ethics of tax evasion and how social perspectives moderate their impacts, the results of this study offer deeper insights into understanding the issue. Practical implications for organizations and society were discussed to reinforce managers’ ethics of tax evasion. The study findings will help organizations and governments establish social programs that will decrease managers’ likelihood to evade taxes, thereby contributing to the development of organizations and the nation.