Crash simulation of wound composite tubes based on multi-level modelling

Modeling cross-lagged effects in psychotherapy mechanisms of change studies is complex and requires careful attention to model selection and interpretation. However, there is a lack of field-specific guidelines. We aimed to (a) describe the estimation and interpretation of cross lagged effects using multilevel models (MLM) and random-intercept cross lagged panel model (RI-CLPM); (b) compare these models' performance and risk of bias using simulations and an applied research example to formulate recommendations for practice. Part 1 is a tutorial focused on introducing/describing dynamic effects in the form of autoregression and bidirectionality. In Part 2, we compare the estimation of cross-lagged effects in RI-CLPM, which takes dynamic effects into account, with three commonly used MLMs that cannot accommodate dynamics. In Part 3, we describe a Monte Carlo simulation study testing model performance of RI-CLPM and MLM under realistic conditions for psychotherapy mechanisms of change studies. Our findings suggested that all three MLMs resulted in severely biased estimates of cross-lagged effects when dynamic effects were present in the data, with some experimental conditions generating statistically significant estimates in the wrong direction. MLMs performed comparably well only in conditions which are conceptually unrealistic for psychotherapy mechanisms of change research (i.e., no inertia in variables and no bidirectional effects). Based on conceptual fit and our simulation results, we strongly recommend using fully dynamic structural equation modeling models, such as the RI-CLPM, rather than static, unidirectional regression models (e.g., MLM) to study cross-lagged effects in mechanisms of change research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

This paper deals with the development and validation of a semi-analytical tire model able to compute the forces at the interface between tire and rim. The knowledge of the forces acting on the rim is of crucial importance for the lightweight design of wheels. The proposed model requires a limited set of data to be calibrated. The model is compared with complete finite element (FE) models of the tire and rim. Despite its simplicity, the semi-analytical model is able to predict the forces acting on the rim, in agreement with the forces computed by complete FE models. The stress state in the wheel rim, computed by the developed semi-analytical model matches fairly well the corresponding stress state coming from experimental tests.

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf<TEX>$\acute{e}$</TEX>s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent structures that characterise and identify the signals. Motivated by the highly non-linear structure of most datasets, we firstly investigate the geometry of manifolds. Manifolds are low dimensional, non-linear structures that are naturally employed to describe sets of strongly related signals such as the images of a 3-D object captured from different viewpoints. However, the use of manifolds in applications is not straightforward due to their usually non-analytic and non-linear form. We propose here a way to `disassemble' a manifold into simpler components by approximating it with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that can represent manifold data accurately while preserving the manifold's structure. To this end, we employ a greedy technique that iteratively merges manifold samples into groups based on the difference of local tangents. We use our algorithm to approximate synthetic and real manifolds and to demonstrate that it is competitive to state-of-the-art techniques. Then, we consider structured sparse representations of signals and we propose a new sparsity model, where signals are essentially composed of a small number of structured {\it molecules }. We define the molecules to be linear combinations of a small number of atoms in a redundant dictionary. Our multi-level model takes into account the energy distribution of the significant signal components in addition to their support. It permits to define typical visual patterns and recognise them in prototypical or deformed form. We define a new structural difference measure between molecules and their deformed versions, which is based on their sparse codes and we create an algorithm for decomposing signals into molecules that can account for different deviations in the internal molecule structure. Our experiments verify the benefits of the new image model in various restoration tasks and they confirm that the development of proper models that extend the mere notion of sparsity can be very useful for various inverse problems in imaging. Finally, we investigate the problem of learning molecule representations directly in the sparse code domain. We constrain sparse codes to be linear combinations of a few, possibly deformed, molecules and we design an algorithm that can learn the structure from the codes without transforming them back into the signal domain. To this end, we take advantage of our structural difference which is based on the sparse codes and we devise a scheme for representing the codes with molecules and learn the molecules at the same time. To illustrate the effectiveness of our proposed algorithm we apply it to various synthetic and real datasets and we compare the results with traditional sparse coding and dictionary learning techniques. From the experiments, we verify the superior performance of our scheme in interpreting and recognising correctly the underlying structure. In short, in this thesis we are interested in low-dimensional, structured models. Among the various choices, we focus on manifolds and sparse representations and we propose schemes that enhance their structural properties and highlight their effectiveness in signal representations.

This study examines the effects of deductibles, employee premium contributions, and the availability of tax-sheltered health savings accounts on employee selection into health insurance plans after passage of the Affordable Care Act, and particularly after implementation of the individual mandate. In doing so, a utility-based structural demand model is applied to market-level macro data from the Kaiser Family Foundation and the Health Research Educational Trust. Results from random coefficients models indicate that for an average consumer, price sensitivity for employer-sponsored health insurance plans varies by age, wage, health condition and gender. Employees who are older, obese, or female are less sensitive to deductibles and premiums than younger and healthier employees, or male employees. These findings suggest that high deductible health plans that combine a tax-sheltered account with higher deductibles will negatively impact the utility of older employees and females as compared to younger employees and males.

The use of computational modeling has become very popular and important in many engineering and physical fields, as it is considered a fast and inexpensive technique to support and often substitute experimental analysis. In fact system design and analysis can be carried out through computational studies instead of experiments, that are typically demanding in terms of cost and technical resources; sometimes the systems characteristics and the technical problems make the experiments impossible to perform and the use of computational tools is the only feasible option. Demand of resources for realistic simulation is increasing due to the interest in studying complex and large systems. In these framework smart modeling approaches and model reduction techniques play a crucial role for making complex and large system suitable for simulations. Moreover, it should be considered that often more than one simulation is requested in order to perform an analysis. For instance, if a heuristic method is applied to the optimization of a component, the model has to be run a certain number of times. The same problem arises when a certain level of uncertainty affect the system parameters; in this case also many simulation are required for obtaining the desired information. This is the reason why the use of technique that allows to obtain compact model is an interesting topic nowadays. In this PhD thesis different reduction approaches and strategies have been used in order to analyze three energetic systems involving large domain and long time, one for each reduction approach categories. In all the topic considered, a smart model has been adopted and, when data were available, tested using experimental data. All the model are characterized by large domain and the time involved in the analysis are high in all the cases, therefore a method for compact model achievement is used in all the cases. The considered topics are: • Groundwater temperature perturbations due to geothermal heat pump installations, analyzed trough a multi-level model. • District heating networks (DHN), studied from both the fluid-dynamic and thermal point of view and applied to one of the larger network in Europe, the Turin district heating system (DHS), trough a Proper Orthogonal Decomposition - Radial Basis Function model. • Forest fire propagation simulation carried out using a Proper Orthogonal Decomposition projection model.

Multilevel nested reliability-based design optimization with hybrid intelligent regression for operating assembly relationship

Replication protein A (RPA) is an essential heterotrimeric ssDNA binding protein that participates in DNA repair, replication, and recombination. Though X-ray and NMR experiments have been used to determine three-dimensional structure models of the protein's domain fragments, a complete RPA structural model has not been reported. To test whether the fragment structures faithfully represent the same portions in the native solution-state protein, we have examined the structure of RPA under biologically relevant conditions. We have probed the location of multiple amino acids within the native RPA three-dimensional structure using reactivity of these amino acids toward proteolytic and chemical modification reagents. In turn, we evaluated different structural models by comparing the observed native RPA reactivities with anticipated reactivities based on candidate structural models. Our results show that our reactivity analysis approach is capable of critically assessing structure models and can be a basis for selecting the most relevant from among alternate models of a protein structure. Using this analytical approach, we verified the relevance of RPA fragment models to the native protein structure. Our results further indicate several important features of native RPA's structure in solution, such as flexibility at specific locations in RPA, particularly in the C-terminal region of RPA70. Our findings are consistent with reported DNA-free structural models and support the role of conformational change in the ssDNA binding mechanism of RPA.

Objective To test the validity of the stress-cognition vulnerability-model of depression in Chinese undergraduates.Methods 647 undergraduate students finished the Chinese version of 6 scales in time one.Cognitive Style Questionnaire(CSQ),Response Styles Questionnaire(RSQ-SF),Self-Esteem Questionnaire(SEQ),Dysfunctional Attitudes Scale(DAS),General Social and Academic Hassles Scale for Students(GSASHS),Center for Epidemiologic Studies Depression Scale(CES-DA)follow-up assessment in 1 month later,the General Social and Academic Hassles Scale for Students(GSASHS)scale and Center for Epidemiologic Studies Depression Scale (CES-D)scale were completed.Then,vulnerability factors for depression symptoms were used to predict depression symptoms by Multiple Hierarchical Regression(MHR).Path analysis and structural equation model(SEM)were used to explore integrated vulnerability-stress model of depression in Chinese undergraduates.Results Vulnerability-stress interaction was entered into regression equation.The results showed that the vulnerability-stress interaction provided incremental predictive validity to depressive.Symptoms path analysis showed that negative cognition→rumination→depression was a important pathways to cause depression(β=-0.31,P<0.01).The structural equation model for integrated vulnerability-stress model of depression analysis indicated the fit index:GFI =0.95,CFI=0.94,IFI=0.94,RMSEA=0.085.Conclusion A cognition vulnerability-stress medels of depression in Chinese undergraduates was provided and to be confirmed.The rumination was a important mediated variable. Key words: Cognition; Vulnerability-stress model; Depression

It is hypothesized that sound quality metrics, particularly loudness, sharpness, tonality, impulsiveness, fluctuation strength, and roughness, could all be possible indicators of the reported annoyance to helicopter noise. To test this hypothesis, a psychoacoustic test was conducted in which subjects rated their annoyance levels to synthesized helicopter sounds. After controlling for loudness, a previous analysis using linear regression identified sharpness and tonality as important factors in predicting annoyance, followed by fluctuation strength. The current work focuses on multilevel regression techniques in which the regression slopes and intercepts are assumed to take on normal distributions across subjects. The importance of each metric is evaluated, and the variation of regression parameters among subjects is evaluated using simple models. Then more complete models are investigated, which include the combination of selected metrics and subject-specific effects. While the conclusions from linear regression analysis are affirmed by multilevel analysis, other important effects emerge. In particular, subject-specific intercepts are shown to be more important than subject-specific slopes. In addition, subject-specific slopes for fluctuation strength and sharpness are more important than for tonality. Using a multilevel framework, the relative importance of sound quality metrics is reexamined, and the potential for modeling human annoyance to helicopter noise based on sound quality metrics is explored.

DAGM-fusion: A dual-path CT-MRI image fusion model based multi-axial gated MLP

Towards the fourth industrial revolution or Industry 4.0 with intelligence as the soul, and data as the key, product manufacturing is facing new challenges in the ever-changing dynamic and competitive environment, and process data-driven intelligent NC machining process planning has become an enabling technology for high efficiency and quality manufacturing. However, existing methods are mainly based on static machining feature for the description of the process data, and they are difficult to represent the embedded multi-granularity process knowledge and experience of designers. In this paper, a multilevel structured NC machining process model based on dynamic machining feature for process reuse is proposed. First, the dynamic machining feature is introduced to make up the deficiency of process design intent capture based on static machining feature. Then, the interactions between 3D CAD model and CAM model are revealed to realize the association in the process data. Finally, the multilevel structured NC machining process model embodying multi-granularity process design intent is established to overcome the sematic gap between high-level macro-technological process and low-level micro-process parameters. A prototype system based on UG NX has been developed to verify the effectiveness of the proposed approach.

Modern construction projects face persistent challenges with cost overruns and fragmented management across disconnected service phases. Whole-Process Engineering Consulting (WPEC) addresses these issues by integrating investment decision-making, design, supervision, and cost management into a unified delivery framework. Therefore, this study aims to develop a WPEC competitiveness influencing factor system to identify the key influencing factors and the impact pathways. Firstly, a WPEC competitiveness framework comprising five dimensions and 28 factors is developed. Secondly, the Analytic Hierarchy Process (AHP) is applied to calculate factor weights based on 225 questionnaires. Then, the multi-level structural model is constructed based on Interpretative Structural Modeling (ISM) to identify the critical impact pathways. Finally, BZ Consulting Enterprise was selected as a case study to verify the rationality and practical value. The results show that the Corporate Full-Service Consulting Capability and Corporate Foundational Resources are identified as the core pillars, in addition to highlighting three key pathways—resource-integration drive, legacy-capability transfer, and service-awareness transformation—all of which link foundational drivers to market performance. Theoretically, this study introduces a systematic analytical framework for WEPC by mapping its competitiveness factors into the multi-level structural model. Practically, it enables enterprises to assess their transition readiness and formulate targeted strategies to secure a sustainable competitive advantage in the integrated consulting market.

The relevance of the research is specified by the significant contribution of the oil produced as a result of hydraulic fracturing in the wells in its total production. A correct assessment of the results of actually carried out hydraulic fracturing will allow to develop clear recommendations on the further application of this method of oil production enhancement for the geological and physical conditions of specific fields. It was established that hydraulic fracturing in the well 221 of the Shershnevsky field (the Perm Territory, Russia) led to a change in interaction between wells within the entire element of the development system; it began to work as a single coordinated system. As a result of hydraulic fracturing, there was not just a redistribution of fluid drainage volumes. A synergistic effect arose when fracturing in one well led to an increase in fluid flow rates and coordinated operation of the entire element of the development system. It is likely that hydraulic fracturing in the well 221 led to a significant change in the geological and technological characteristics of the Bobrikovskian deposit of the Shershnevsky field to a greater extent than the volume of the drainage zone of this well. A whole system of channels with reduced filtration resistances appeared instead of a single crack, as is common in the classic representation of hydraulic fracturing. It should be noted that the approach presented in the article is the first very important step in a comprehensive analysis of the effective reservoir development based on the results of field monitoring. In the future, it is necessary to attract more detailed information about the interaction of wells. Only such a multilevel analysis will allow to substantiate the general conclusion about the hydraulic fracturing on the development of a reservoir and to confirm conclusively the effect of wells on each other, which can be individual in different parts of the reservoir.

Heat transfer in flooded evaporators of the refrigeration, air conditioning or process industries is mainly enhanced by modifying the surface structure of evaporator tubes in the micro and/or macro range. To quantify the effect of such modifications, however, the influence of the basic roughness structure on the heated surface has to be separated. Starting from recent publications, experimental results of heat transfer and bubble formation from horizontal copper tubes with different outer diameters (8 or 25 mm) and roughness structures to various boiling liquids are analyzed in this paper to improve our knowledge of the specific events connected with the formation of bubbles at active nucleation sites and their effect on local heat transfer. It is shown that a single, standardized roughness parameter like the (integral) mean roughness height P a is not sufficient to explain the effect of the heating surface structure on nucleate boiling heat transfer. Instead, detailed information on characteristic roughness parameters of the heated surfaces is necessary for the analysis, making it possible to define the size and form of cavities included in the roughness structure and their positions on the surface. An analysis that aims in this direction is given in a separate contribution to this special issue by A. Luke, who prepared the surfaces and provided the basic data on the set of standardized roughness parameters, the probability distributions of which are used in this paper.