Simple Structural Model Research Articles

Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). This paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.

Spiking neural P systems with long-term potentiation and depression

Spiking neural P systems (SNP systems) are parallel, distributed, low energy consuming, non-deterministic, interpretable and simple structured computational models abstracted from biological neural systems. In this work, to make SNP systems have the ability to adjust synaptic weights according to changes in the environment and boost SNP systems' learning ability and intelligence, long-term potentiation/depression mechanisms in the signal transmission of two neurons are abstracted from biological neural systems and introduced into SNP systems. As result, a type of novel SNP system, called SNP systems with long-term potentiation and depression (LTPD-SNP systems), is developed. Based on novel synaptic weight learning rules in LTPD-SNP systems, the weights of synapses change dynamically depending on the intensity and timing of the spikes passing through them and their own properties. The universality of LTPD-SNP systems as number generation and acceptance devices is proved. A uniform solution to SAT problem is designed to study the computational efficiency of the LTPD-SNP systems. Simultaneously, an anomaly detection method for AC engines based on LTPD-SNP systems is constructed to show how the system can solve a learning task. This work improves the learning ability and intelligence of SNP systems, and offers some ideas for creating brain-like intelligent computational models.

Comparison of arterial wall models in fluid–structure interaction simulations

AbstractMonolithic fluid–structure interaction (FSI) of blood flow with arterial walls is considered, making use of sophisticated nonlinear wall models. These incorporate the effects of almost incompressibility as well as of the anisotropy caused by embedded collagen fibers. In the literature, relatively simple structural models such as Neo-Hooke are often considered for FSI with arterial walls. Such models lack, both, anisotropy and incompressibility. In this paper, numerical simulations of idealized heart beats in a curved benchmark geometry, using simple and sophisticated arterial wall models, are compared: we consider three different almost incompressible, anisotropic arterial wall models as a reference and, for comparison, a simple, isotropic Neo-Hooke model using four different parameter sets. The simulations show significant quantitative and qualitative differences in the stresses and displacements as well as the lumen cross sections. For the Neo-Hooke models, a significantly larger amplitude in the in- and outflow areas during the heart beat is observed, presumably due to the lack of fiber stiffening. For completeness, we also consider a linear elastic wall using 16 different parameter sets. However, using our benchmark setup, we were not successful in achieving good agreement with our nonlinear reference calculation.

Matching Pell Grants: Implications for College Debt and Parental Transfers

We study the causal effect of the net price of college on student debt, schooling investment, and parental transfers by exploiting a natural experiment. A large public US university introduced a program to match Pell Grants in academic year 2019-2020. We use a difference-in-difference approach to measure the impact of this policy on the variables of interest and interpret our findings using a simple structural model of human capital investment under credit constraints.

Performance evaluation of a pipe-embedded phase change material (PE-PCM) roof integrated with solar collector

Utilizing renewable energy in buildings is important for the development of zero-energy buildings to achieve the goals of carbon peak and carbon neutrality. A novel roof with a pipe-embedded phase change material (PE-PCM) integrated with a solar collector is proposed to fully utilize solar energy and improve building efficiency. The PE-PCM layer is attached to the inner side of the roof for effective utilization of solar energy and enables all-day indoor heating via thermal storage. In this study, a simplified model of a PE-PCM structure was developed based on its physical configuration, which included a variable thermal capacitance and thermal resistance model (VarRC model). The simplified PE-PCM roof model integrated with the VarRC model is validated using a numerical model. Additionally, a platform for a room with the PE-PCM roof and solar collector is developed to investigate the system performance. The results showed that the heating demand can be met, except under extreme weather conditions. When the indoor temperature is controlled above 17 °C by auxiliary heating, the total energy required for heating the room with the PE-PCM roof can be reduced by 54% compared to that of a conventional roof room.

Thermoelastic damping in micro-scale T-, U- and Z-shaped frame structures using wave propagation approach

Studying the thermo-mechanical coupling behavior and establishing the thermoelastic damping (TED) model are essential for improving the quality factor of the microelectromechanical resonator. In this work, an analytical TED model for planar frame resonators is first developed using the wave propagation approach. The method is illustrated for typical T-, U- and Z-shaped frame structures and can be extended to more complex frame structures. The frame is viewed as a set of beam segments interconnected by rigid joints, each following the Euler-Bernoulli beam theory. Based on the wave propagation approach, the wave transmission and reflection properties at discontinuities are determined taking into account the flexural and longitudinal motions. The displacement fields of the frame structure are established for both free and forced vibrations. The coupled temperature field of the frame is obtained by solving for the temperature variation arising from the flexural and longitudinal vibrations separately. Finally, the expression of TED is achieved by its energy definition. The validity of the present TED model is confirmed by comparison with finite element (FE) simulations. Numerical results indicate that the TED behavior of frame structures is too complicated to be predicted by previous TED models of simple structures. The often-overlooked longitudinal motion will cause a dramatic TED reduction around specific frequencies. The overall TED is determined by all segments together, and the influence of a segment is reflected by its energy share. Moreover, the effects of different boundary conditions and geometric parameters on TED are evaluated. Practical conclusions to guide the design of high Q-factor resonators are drawn based on the TED behavior of frame structures as summarized from the numerical study.

GOODHART'S LAW AND MACHINE LEARNING: A STRUCTURAL PERSPECTIVE

AbstractWe develop a simple structural model to illustrate how penalized regressions generate Goodhart bias when training data are clean but covariates are manipulated at known cost by future agents. With quadratic (extremely steep) manipulation costs, bias is proportional to Ridge (Lasso) penalization. If costs depend on absolute or percentage manipulation, the following algorithm yields manipulation‐proof prediction: Within training data, evaluate candidate coefficients at their respective incentive‐compatible manipulation configuration. We derive analytical coefficient adjustments: slopes (intercept) shift downward if costs depend on percentage (absolute) manipulation. Statisticians ignoring manipulation costs select socially suboptimal penalization. Model averaging reduces these manipulation costs.

Assessing the impact of single or short-term administration on a therapy’s cost-effectiveness: a hypothetical disease-agnostic model

AimsAssessing the value of single or short-term therapies (SSTs) within traditional cost-effectiveness analyses (CEAs) has been a topic of discussion as the number of SSTs increases, particularly regarding the effect of discounting on valuation. To quantify the impact of discounting in economic evaluations, a CEA of a hypothetical SST and equivalent chronic therapy was conducted using standard methods.Materials and methodsA lifetime Markov model was developed for a hypothetical chronic, progressive disease that could be treated with an SST, chronic therapy, or no novel treatment, termed standard of care (SoC). Incremental cost-effectiveness ratios (ICERs) with quality-adjusted life years (QALYs) comparing SST vs. SoC and an equivalent chronic therapy vs. SoC were assessed from a payer perspective. Both treatments had equal benefits and undiscounted lifetime costs; 3% discounting was applied to costs/benefits in the base case, and the impact of discounting was assessed.ResultsIn the base case example, both the SST and equivalent chronic therapy vs. SoC had ICERs of $86,000/QALY without discounting. With 3% discounting, the ICER for the SST increased by 116% ($186,000/QALY) while the ICER for the chronic therapy increased by 10% ($95,000/QALY) despite equal clinical benefit. In scenario analyses, the ICER of the SST was consistently higher than the equivalent chronic therapy across a range of assumptions/inputs. Varying the cost/benefit discount rates had a greater impact on the SST. Differences in the ICERs between the therapies increased with increasing life expectancy/time horizon.LimitationsThe simple model structure may not be reflective of acute or more complex diseases. Also, the scenario of perfect equivalency in efficacy and lifetime costs is hypothetical.ConclusionsThis quantitative assessment showed the extent to which SST CEAs are highly sensitive to discounting, resulting in worse value assessments for SSTs than equivalent chronic therapies.

On the Impact of Prior Experiences in Car-Following Models: Model Development, Computational Efficiency, Comparative Analyses, and Extensive Applications.

A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.

Improved identification of stiffness coefficients of non intrusive nonlinear geometric reduced order models of structures

Significant, successful efforts have been devoted in recent years to the non intrusive construction from finite element software of reduced order models of the nonlinear geometric response of structures. A key assumption in these models is that the elastic restoring forces can be represented as cubic polynomials of their generalized coordinates. However, there has been only very limited discussion of whether this representation is appropriate, how deviations from this polynomial form affect the identification of its coefficients and ultimately the prediction of the structural response, and how current identification strategies can be improved.These questions are the focus of the present investigation which considers first a simple structural model that exhibits the potential for snap-through and thus represents an interesting validation case with strongly nonlinear geometric behavior. It is shown that the force–displacement relationship which can be derived in closed form is not exactly a cubic polynomial but can be very closely modeled by one. However, the match between exact curve and polynomial fit is very sensitive to small changes in the linear, quadratic, and cubic stiffness coefficients and thus the prediction of the response may range from excellent to significantly in error depending on how the identification of the coefficients is performed. In particular, it is found that a cubic approximation obtained by Taylor series around the undeformed configuration is not very accurate post snap-through.Based on these findings, two strategies are proposed to improve the identification of the stiffness coefficients of these reduced order models. The first one is a tuning procedure in which a limited number of key coefficients of the model are adjusted to fit additional static response data. The second approach performs the identification of the stiffness coefficients at a series of different levels, as opposed to one as currently performed, and seeks the most appropriate value of each coefficient based on a “small change” criterion.The applicability of these two novel strategies is demonstrated on several challenging structural models: a clamped–clamped curved beam undergoing snap-through as well as a curved panel and a hat stiffened one. It is found that the reduced order models identified via the tuning or multi-level strategies not only lead to more accurate predictions of the structural response as compared to the original finite element model but also are more computationally stable than their counterparts identified with the current single-level strategy.

GNSS Antenna Pattern Prediction and Placement Optimization: A Prototype Method Using Machine Learning to Aid Complex Electromagnetic Simulations Validated on a Vehicle Model

An antenna’s radiation pattern is dependent on its geometrical characteristics and its antenna’s surroundings, materials, and geometries. As such, to predict the antenna’s performance in complex environments, such as that of small antennas on large vehicles, it is necessary to obtain a model that represents such a full scenario, so that the simulation may be accomplished in the process of antenna design and placement. Due to the complex and electrically large nature of some electromagnetic problems, the detailed representation, even for a simplified model, may imply a large computational effort, both in terms of time and memory, is needed to perform the simulation. This paper evaluates how machine learning models can be used to mitigate the computational effort required to predict the behavior of antennas requiring complex modeling. It is proposed to start from a more simplified model of the electromagnetic structure to obtain a prediction for the correct solution, without needing to simulate the full structure in every iteration, and to combine this with prediction algorithms to obtain the solution of the full problem. The proposed solution uses convolutional neural networks (U-Net) of a certain accuracy to help with the correct placement of small antennas on autonomous vehicles. The standard approach requires the simulation of a full model at each test position, requiring high computational time and memory. With this new proposal, it is possible to analyze more positions and radiation patterns in a much shorter time, and with less memory, when compared with the solution from the full model. Along with this methodology for each simulation, a Bayesian optimizer is proposed to improve the search process for the best location, leading to a reduction in the required steps. This methodology was applied to support the correct positioning of a GNSS antenna with reference to a set of performance indicators required for autonomous vehicles, but it can be also applied to larger and more complex structures, allowing one to reduce the simulation time of a large electromagnetic structure and the search time for the optimum location.

The NB5I

Abstract: Personality is usually measured by means of self-ratings. Despite some drawbacks, the method is here to stay, and improving on it, particularly regarding social desirability, is essential. One way to do this is evaluative neutralization, that is, to rephrase items such that it is less obvious to the respondent what would be a desirable response. We present a 120-item evaluatively neutralized five-factor inventory and compare it to the IPIP-NEO ( Goldberg et al., 2006 ). Psychometric analyses revealed that the new inventory has high factor homogeneity, relatively independent facets with acceptable homogeneity and normally distributed ratings, and relatively evaluatively neutral ratings (as indicated by the level of item popularity). In sum, this new inventory captures the same personality variance as other five-factor inventories but with less influence from individual differences in evaluative responding, resulting in less correlation between factors and a factor structure more in line with the simple structure model than many other five-factor inventories. Evaluatively neutralized inventories should be particularly useful when the factor structure is central to the research question and focuses on discriminant validity, such as identifying theoretically valid relationships between personality traits and other concepts.

A novel approach to dissect ions occupancy and dissociation constant of individualbinding sites within the K+-channel selectivity filter.

K+-channels permeate Rb+ as well as they do K+, although with a lower single channel conductance. KcsA crystal structure displays four K+ bound to its selectivity filter (SF); however, when crystallized in Rb+, ions were only found at the binding sites 1, 3, and 4. This observation explains the lower single conductance of K+-channels in rubidium. We reported a KcsA mutant (G77A) that stabilized the SF in the 2-4 ion bound configuration with K+ ions coordinated in the 2nd and 4th binding sites. Crystallizing the KcsA-G77A structure in rubidium interestingly showed Rb+ ions bound to the 1st and 4th binding sites. Like in the wild-type channel, the 2nd binding site was uncapable of binding rubidium. Therefore, we used the G77A to measure the 1st binding site dissociation constant for Rb+ by Isothermal Titration Calorimetry (ITC). Previously, computational studies have suggested that: 1) the SF's 4th ion binding site can be occupied by K+ or Na+ and hence it should not contribute to the SF's K+ binding affinity and 2) the 2nd binding site is responsible for the K+ binding affinity displayed by K+-channels SF. In here, we are showing that in K+-channels the 2nd binding site is indeed responsible for K+ binding affinity while the 1st site is responsible for the Rb+ ion binding affinity, since the G77A mutant (a simplified structural model for the SF) showed a wild-type like Rb+ dissociation constant. Therefore, we propose that in K+-channels the 1st ion binding site is responsible for the high rubidium over sodium selectivity shown by KcsA and potentially other potassium channels. Our advances in applying this approach for the alkali metal ion series will be presented.

Toward 68Ga and 64Cu Positron Emission Tomography Probes: Is H2dedpa-N,N'-pram the Missing Link for dedpa Conjugation?

H2dedpa-N,N'-pram (H2L1), a new chelator derived from the hexadentate ligand 1,2-bis[[(6-carboxypyridin-2-yl)methyl]amino]ethane (H2dedpa), which incorporates 3-propylamine chains anchored to the secondary amines of the ethylenediamine core of the latter, has emerged as a very promising scaffold for preparing 68Ga- and 64Cu-based positron emission tomography probes. This new platform is cost-effective and easy to prepare, and the two pendant primary amines make it versatile for the preparation of bifunctional chelators by conjugation and/or click chemistry. Reported herein, we have also included the related H2dedpa-N,N'-prpta (H2L2) platform as a simple structural model for its conjugated systems. X-ray crystallography confirmed that the N4O2 coordination sphere provided by the dedpa2- core is maintained at both Ga(III) and Cu(II). The complex formation equilibria were deeply investigated by a thorough multitechnique approach with potentiometric, NMR spectrometric, and UV-vis spectrophotometric titrations, revealing effective chelation. The thermodynamic stability of the Ga(III) complexes at physiological relevant conditions is slightly higher than that of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA), the common and clinically approved chelator used in the clinic [pGa = 19.5 (dedpa-N,N'-pram) and 20.8 (dedpa-N,N'-prpta) versus 18.5 (DOTA) at identical conditions], and significantly higher for the Cu(II) complexes [pCu = 21.96 (dedpa-N,N'-pram) and 22.8 (dedpa-N,N'-prpta) versus 16.2 (DOTA)], which are even more stable than that of the parent ligand dedpa2- (pCu = 18.5) and that of 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) (pCu = 18.5). This high stability found for Cu(II) complexes is related to the conversion of the secondary amines of the ethylenediamine core of dedpa2- into tertiary amines, whereby the architecture of the new H2L1 chelator is doubly optimal in the case of this metal ion: high accessibility of the primary amine groups and their incorporation via the secondary amines, which contributes to a significant increase in the stability of the metal complex. Quantitative labeling of both chelators with both radionuclides ([68Ga]Ga3+ and [64Cu]Cu2+) was observed within 15 min at room temperature with concentrations as low as 10-5 M. Furthermore, serum stability studies confirmed a high radiochemical in vitro stability of all systems and therefore confirmed H2L1 as a promising and versatile chelator for further radiopharmaceutical in vivo studies.

The safety evaluation method of steel enterprises based on deep learning

As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises.

Photovoltaic power prediction under insufficient historical data based on dendrite network and coupled information analysis

In recent years, the installed capacity of photovoltaic solar energy has been increasing year by year. The existing power prediction methods are difficult to achieve reliable power generation prediction for PV equipment that has just been put into service. This poses great difficulties for power system management and dispatching. Therefore, establishing a reliable PV power prediction model for this situation is important for the rapid integration of new-built PV installations into the power system for energy management and dispatching. In this paper, an ultra-short-term PV power prediction method based on coupled information analysis and a dendritic network is proposed. First, an improved Hampel filter is proposed to improve the accuracy of anomaly data processing by analyzing the information between strongly coupled variables. In addition, a dendritic network modeling method is introduced for PV generation prediction. Compared to other solutions, this prediction approach relies on very little historical data to achieve reliable predictions and also features a simple model structure and high generalization. Forty different sets of tests were conducted according to different weather conditions and data conditions. The experimental results show that the proposed method can achieve higher prediction performance and stability compared with the benchmark model.

Analysis of the New Organizational Structure of the Kasepuhan Indigenous People through the Analytical Hierarchy Process (AHP) Method

An organizational structure that can realize the vision, mission, goals and strategies of any organization, including the Kasepuhan Banten Kidul indigenous people, is a critical factor in developing its existence. This structure facilitates communication and coordination, so that the tasks of empowering ancestral trusts in the form of preserving nature and agriculture (pamakayaan) can be accomplished properly. The qualitative descriptive method is used as an analytical knife which is then conducted by the hierarchical analysis process (AHP). The structure that has been owned and used for hundreds of years is in the form of a line (line organization).The most preferred organizational structure from the perspective of the Kasepuhan Banten Kidul indigenous people is the Divisional Organizational Structure with the highest score (0.68) compared to other alternative types of organizational structures, namely simple (line) and functional structural models. The Consistency Ratio (CI/IR) value is 0.02 which means below 10%.

Credit default swaps, the leverage effect, and cross-sectional predictability of equity and firm asset volatility

Leverage represents both a fundamental component of equity volatility and a long-run selection variable. Based on this premise, we investigate the influence of leverage on the long-run cross-sectional predictability of future realized equity volatility. Leverage makes equity volatility significantly less predictable than underlying firm asset volatility, a result that is robust to different predictors of future realized volatility: credit default swap implied, historical, and option implied volatility. A simple model of optimal capital structure, wherein companies maximize tax benefits subject to a common maximum default probability (minimum credit rating) target, helps explain this finding.

Seismic Response Analysis of 2-Storey Mosque Structure Using Diagonal Brace Damper and Toggle Brace Damper

Mosque structure are typical public building where Muslim follower doing their worship and social activities. These structures are common in Indonesia where the dangerous earthquake usually happens. This paper is aimed to study of passive control of a typical 2-storey Mosque structure using diagonal and toggle brace damper. At the first, we build a simplified structure model of 2 degree of freedom (2DOF) for controlled and uncontrolled system. In this study, we create 5 cases where case 1 as uncontrolled system. Then we compared the dynamic responses of the structure cases subjected to 1940 El Centro earthquake record using time history analysis (THA). The effect of the geometric configuration of the damper attached to the structure which influenced by the magnification factor will be studied.

A new analytical method for calculating the strain of oil and gas pipeline under a normal fault dislocation

AbstractOil and gas pipelines that go through an active normal fault often deform largely and cause serious disasters. On the basis of the beam theory of the elastic foundation and assuming a shape function for the deformed pipeline, a new analytical method is proposed to calculate the pipeline strain under a normal fault. The length of the deformed pipeline is calculated by using a simplified structural mechanical model. Moreover, a three‐dimensional solid finite element model (3D FEM) is developed to calculate the pipeline strain under different working conditions. The developed analytical solution and the numerical results of 3D FEM are compared with the results of the two traditional methods (the dissipated energy method and the Karamitros method). The strain development law predicted by the proposed method and the 3D FEM is in good agreement with that of the dissipated energy method and the Karamitros method. Compared with the existing models, the new analytical method is simpler and more efficient.