Heuristic Model Research Articles

Development of mechanics-based fragility curves for the Italian masonry school asset

This article presents the derivation of a fragility model for the Italian masonry school building asset, comprising 265 sets of fragility curves for as many building types, classified on the basis of few parameters: construction age, number of stories, plan area, and type of masonry (i.e. with regular or irregular pattern). The fragility assessment was carried out by means of parametric analyses, generating more than 7500 samples which were then analyzed through the mechanics-based procedure Vulnus. Sample fragilities were then linearly combined to obtain fragility curves consistent with the adopted taxonomy based on few parameters. A macroseismic–heuristic model from the literature was used to extend the fragility model to five damage states, according to the European Macroseismic Scale (EMS98). The proposed model was compared to empirical information in terms of observed damage on three existing schools and fragility curves recently derived by processing data of school damaged by the 2009 L’Aquila earthquake, showing a satisfactory agreement. In addition, a comparison with fragility sets for residential buildings was carried out. Both fragility models were developed with the same procedure, so as to point out differences between schools and ordinary buildings. Similar fragilities were observed for schools and residential buildings built before 1945, whereas for later periods, schools showed a higher fragility than the residential asset. Finally, seismic damage maps were developed at national scale showing the distribution of expected damage as a possible application of the derived model.

Mathematical Programming Formulations for the Berth Allocation Problems in Container Seaport Terminals

Background: Improving the performance of marine terminals is one of the major concerns of both researchers and decision-makers in the maritime transportation sector. The problem of container storage planning and the berth allocation problem (BAP) are the two mainstays of optimizing port operations. Methods: In this work, we address these two issues, proposing two mathematical models that operate sequentially and are applicable to both static and dynamic cases. The first developed model is a mixed-integer linear problem model aimed at minimizing vessel traffic time in the port. The second model developed is a multi-objective optimization model based on goal programming (GP) to minimize both container transfer time and the number of storage areas (minimizing container dispersion). Results: The robustness of the proposed models has been proven through a benchmark with tests using data from the literature and real port data, based on the IBM ILOG CPLEX 12.5 solver. Conclusions: The two developed mathematical models allowed the both minimization of the transfer time and the number of used storage areas, whatever the number of operations handling companies (OHCs) operating in the seaport and for both static and dynamic cases. We propose, as prospects for this work, the development of a heuristic model to deal with the major instances relating to the case of large ports.

Examining the influence of source factors and content characteristics of influencers' post on consumer engagement and purchase intention: A moderated analysis

The current research investigates the impact of fashion influencers on consumer engagement and purchase intention. Guided by the heuristic systematic model (HSM), this paper identifies heuristic and systematic factors of an influencer's post that contributes to consumer engagement and purchase intention. Data collected from 471 respondents was studied through path analysis. The results reveal that consumer engagement mediates between source credibility, content characteristics and purchase intention. Furthermore, the findings indicate that product involvement moderates the relationship between source factors, content characteristics and consumer engagement. The study adds to the extant literature and provides managerial insights for the fashion industry by suggesting ways to choose the right influencer and build stronger relationships with consumers.

Velocity Measurements of Powdered Rock at Low Confining Pressures and Comparison to Lunar Shallow Seismic Velocity

AbstractSeismic methods will be useful for future lunar near‐surface characterization, and high‐fidelity elastic models will be required to aid interpretation of seismic observations. To develop an elastic lunar near‐surface model, we performed ultrasonic velocity measurements of lunar regolith simulant at low confining pressure and developed a rock physics model calibrated to these measurements. Grain contact models based on Hertz‐Mindlin theory produce accurate results at high confining pressure (i.e., several hundred meters or more burial depth) but historically fail to predict observed velocities in unconsolidated media at low pressure. Therefore, we heuristically modified existing models to fit our measured data over a range of porosities and confining pressures. To compare with Apollo 14 and 16 active seismic experiments, we used our new heuristic rock physics model to produce lunar subsurface velocity profiles. We performed ray tracing through our velocity profiles to calculate seismic traveltime, which results in good agreement with first arrivals interpreted from the Apollo experiments. Our model suggests a slightly higher velocity‐pressure dependence than inferred from in situ measurements, which may be due to porosity reduction in the lunar regolith from impact‐induced and natural vibrations.

Proxenia as a Model for Early Christian Host-Leaders

To mitigate the costs and risks of travel, Greek communities utilized the formal social institution of proxenia. The honorific title of proxenos (“public host”) was bestowed upon someone in another community who provided travelers with hospitality services, such as physical accommodation, social access, and legal protections. This social institution of proxenia, similar to but more specific than ancient patronage or benefaction, offers a fruitful heuristic model for interpreting travel among early Christians and the role of host-leaders within the emerging movement. Itinerant Christians would have utilized the proxenos-paradigm and cultivated proxenos-like relationships with prominent Christian hosts. This paper examines how Philemon and Phoebe functioned as proxenos-like hosts for Christians traveling through their respective cities. We conclude with discussions regarding the prominence and authority of proxenia-like host-leaders within early Christian social networks. Such people with the capacity to facilitate travel and grant social inclusion amassed significant power, as they became the centralized nodes enabling social connectivity..

How and why do species break a developmental trade-off? Elucidating the association of trichomes and stomata across species.

Previous studies have suggested a trade-off between trichome density (Dt) and stomatal density (Ds) due to shared cell precursors. We clarified how, when, and why this developmental trade-off may be overcome across species. We derived equations to determine the developmental basis for Dt and Ds in trichome and stomatal indices (it and is) and the sizes of epidermal pavement cells (e), trichome bases (t), and stomata (s) and quantified the importance of these determinants of Dt and Ds for 78 California species. We compiled 17 previous studies of Dt-Ds relationships to determine the commonness of Dt-Ds associations. We modeled the consequences of different Dt-Ds associations for plant carbon balance. Our analyses showed that higher Dt was determined by higher it and lower e, and higher Ds by higher is and lower e. Across California species, positive Dt-Ds coordination arose due to it-is coordination and impacts of the variation in e. A Dt-Ds trade-off was found in only 30% of studies. Heuristic modeling showed that species sets would have the highest carbon balance with a positive or negative relationship or decoupling of Dt and Ds, depending on environmental conditions. Shared precursor cells of trichomes and stomata do not limit higher numbers of both cell types or drive a general Dt-Ds trade-off across species. This developmental flexibility across diverse species enables different Dt-Ds associations according to environmental pressures. Developmental trait analysis can clarify how contrasting trait associations would arise within and across species.

성인학습자의 무형식학습 프로세스에 영향을 미치는 요인들에 대한 연구

The purpose of this study is to identify the meaning of informal learning for adult learners in lifelong learning by reviewing discourses in pedagogy related to informal learning and introducing heuristic model for practical research on informal learning. To this end, after reviewing the main perspectives on the meaning of informal learning, which has been conventionally discussed in educational research, in detail, the trends in informal learning research in three areas primarily observed in lifelong learning (informal learning in the workplace, multicultural informal learning, and community informal learning) were examined. Adult learners’ informal learning occurs through interactions between society, culture, media, or members in the everyday learning environment based on sociocultural context to which they belong, such as society, community, and workplace. Based on this, a heuristic model of informal learning was introduced that takes into account the social conditions of learners that can produce continuous informal learning as well as individual learner aspect. In the end, as a comprehensive perspective to understand human learning in the further research, the research direction for informal learning research and the expectation for the analytical foundation of practical research to support adult learners' informal learning was suggested.

Vehicle vibration test platform structure design and control strategy optimization

AbstractFor the needs of vehicle vibration test platform with high precision, large load capacity and fast response, the three‐dimensional model design and analysis of vehicle vibration test platform are carried out; in order to improve the motion performance of the platform, a vibration test plat‐form control strategy combining hybrid heuristic algorithm and PID control is proposed. Based on the designed 3D model parameters, the single‐channel mathematical model of the servo‐electric cylinder is derived and a hybrid heuristic algorithm PID optimization model is established to compare and analyze the control performance of the platform with the Ziegler‐Nichols method PID. The results show that the step system overshoot is 3.80% and the dynamic performance of the system is significantly improved when the hybrid heuristic algorithm PID control is used. The simulation system model of vehicle vibration test platform control is established, and the operation results show that the platform is closer to the input signal in the spatial position change curve when the hybrid heuristic algorithm PID control is used. Its maximum displacement error is 0.09 mm, and the motion accuracy of the system is improved by 61% compared with the Ziegler‐Nichols method PID control.

Entity Footprinting: Modeling Contextual User States via Digital Activity Monitoring

Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that users will need to perform digital tasks. There have been methods developed to automatically detect the tasks of a user. However, these studies typically support only specific applications and tasks, and relatively little research has been conducted on real-life digital activities. This article introduces user state modeling and prediction with contextual information captured as entities, recorded from real-world digital user behavior, called entity footprinting —a system that records users’ digital activities on their screens and proactively provides useful entities across application boundaries without requiring explicit query formulation. Our methodology is to detect contextual user states using latent representations of entities occurring in digital activities. Using topic models and recurrent neural networks, the model learns the latent representation of concurrent entities and their sequential relationships. We report a field study in which the digital activities of 13 people were recorded continuously for 14 days. The model learned from this data is used to (1) predict contextual user states and (2) predict relevant entities for the detected states. The results show improved user state detection accuracy and entity prediction performance compared to static, heuristic, and basic topic models. Our findings have implications for the design of proactive recommendation systems that can implicitly infer users’ contextual state by monitoring users’ digital activities and proactively recommending the right information at the right time.

Maximizing marketing impact: heuristic vs ensemble models for attribution modeling

Purpose Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models. Design/methodology/approach The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile). Findings Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value. Originality/value To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

A hybrid model for VNF deployment capable of responding to online requests at network edge

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the Whale Optimization Algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

Bifurcation delay and front propagation in the real Ginzburg-Landau equation on a time-dependent domain.

This work analyzes bifurcation delay and front propagation in the one-dimensional real Ginzburg-Landau equationwith periodic boundary conditions on isotropically growing or shrinking domains. First, we obtain closed-form expressions for the delay of primary bifurcations on a growing domain and show that the additional domain growth before the appearance of a pattern is independent of the growth time scale. We also quantify primary bifurcation delay on a shrinking domain; in contrast with a growing domain, the time scale of domain compression is reflected in the additional compression before the pattern decays. For secondary bifurcations such as the Eckhaus instability, we obtain a lower bound on the delay of phase slips due to a time-dependent domain. We also construct a heuristic model to classify regimes with arrested phase slips, i.e., phase slips that fail to develop. Then, we study how propagating fronts are influenced by a time-dependent domain. We identify three types of pulled fronts: homogeneous, pattern spreading, and Eckhaus fronts. By following the linear dynamics, we derive expressions for the velocity and profile of homogeneous fronts on a time-dependent domain. We also derive the natural "asymptotic" velocity and front profile and show that these deviate from predictions based on the marginal stability criterion familiar from fixed domain theory. This difference arises because the time dependence of the domain lifts the degeneracy of the spatial eigenvalues associated with speed selection and represents a fundamental distinction from the fixed domain theory that we verify using direct numerical simulations. The effect of a growing domain on pattern spreading and Eckhaus front velocities is inspected qualitatively and found to be similar to that of homogeneous fronts. These more complex fronts can also experience delayed onset. Lastly, we show that dilution-an effect present when the order parameter is conserved-increases bifurcation delay and amplifies changes in the homogeneous front velocity on time-dependent domains. The study provides general insight into the effects of domain growth on pattern onset, pattern transitions, and front propagation in systems across different scientific fields.

Optimal channel selection for tri-band Wi-Fi in a residential scenario

The growing use of Internet of Things devices and the increasing demand for high-speed, reliable, and secure wireless connectivity pose significant challenges for existing wireless networking solutions in modern smart homes. As such, there is an increasing urgency for the development of advanced and effective wireless technologies that can fulfil the new requirements for interconnected devices and services deployed in households. Tri-band Wi-Fi 6E equipment addresses these needs by reducing the network congestion and enhancing the performance across the 2.4 GHz, 5 GHz, and 6 GHz bands. However, improper band management can lead to frequent interference and network issues. As such, this work introduces a dynamic Wi-Fi link orchestration solution that follows a heuristic model. This approach aims to optimize the network layout and channel allocation based on device metrics, utilizing the EasyMesh specification for simplified network setup and management. The model was implemented and tested in a residential environment network, using EasyMesh and tri-band Wi-Fi 6E devices. Results show the effectiveness of the model in improving network capacity and adapting the links to the current traffic, outperforming the initial and base network configuration.

TMAS: A transaction misbehavior analysis scheme for blockchain

Blockchain-based cryptocurrencies, such as Bitcoins, are increasingly popular. However, the decentralized and anonymous nature of these currencies can also be (ab)used for nefarious activities such as money laundering, thus reinforcing the importance of designing tools to effectively detect malicious transaction misbehaviors. In this paper, we propose TMAS, a transaction misbehavior analysis scheme for blockchain-based cryptocurrencies. Specifically, the proposed system includes ten features in the transaction graph, two heuristic money laundering models, and an analysis method for account linkage, which identifies accounts that are distinct but controlled by an identical entity. To evaluate the effectiveness of our proposed indicators and models, we analyze 100 million transactions and compute transaction features, and are able to identify a number of suspicious accounts. Moreover, the proposed methods can be applied to other cryptocurrencies, such as token-based cryptocurrencies (e.g., Bitcoins) and account-based cryptocurrencies (e.g., Ethereum).

Mach number influence on low-pressure turbine flutter

The effect of the Mach number on the vibration amplitude trends of an isolated low-pressure turbine rotor is described. The study utilizes an analytical model correlating the aerodynamic work and the dry-friction work. Aerodynamic damping has been obtained using a frequency domain linearized Navier-Stokes solver, whereas friction damping has been modeled using a heuristic micro-slip model calibrated with the experimental data. In order to validate the analytical predictions, an extensive experimental campaign was conducted. Unsteady pressure transducers flushed mounted in the outer casing, one chord downstream of the trailing edge of the rotor, were used to characterize the vibration of the rotor. The analysis reveals a significant influence of the Mach number on the work balance between aerodynamic and mechanical components. It is observed that the vibration amplitude and the flutter-induced unsteady pressure perturbations of low-pressure turbine rotor blades notably increases when the exit Mach number does.

Adaptive Ensemble of Multi-Kernel Gaussian Process Regressions Based on Heuristic Model Screening for Nonparametric Modeling of Ship Maneuvering Motion

Abstract Gaussian process regression (GPR) is a commonly used approach for establishing the nonparametric models of ship maneuvering motion, and its performance depends on the selection of the kernel function. However, no single kernel function can be universally applied to all nonparametric models of ship maneuvering motion, which may compromise the robustness of GPR. To address this issue, an adaptive ensemble of multi-kernel GPRs based on heuristic model screening (AEGPR-HMS) is proposed in this paper. In the proposed method, four kernel functions are involved in constructing the ensemble model. The HMS method is introduced to determine the weights of individual-based GPR models, which can be adaptively assigned according to the baseline GPR model. To determine the hyper-parameters of these kernel functions, the genetic algorithm is also employed to compute the optimal values. The KVLCC2 tanker provided by the SIMMAN 2008 workshop is used to validate the performance of the proposed method. The results demonstrate that the AEGPR-HMS is an efficient and robust method for nonparametric modeling of ship maneuvering motion.

A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in Kilometer-Scale Forecasts

Abstract Detection and tracking of tropical cyclones (TCs) in numerical weather prediction model outputs is essential for many applications, such as forecast guidance and real-time monitoring of events. While this task has been automated in the 1990s with heuristic models, relying on a set of empirical rules and thresholds, the recent success of machine learning methods to detect objects in images opens new perspectives. This paper introduces and evaluates the capacity of a convolutional neural network based on the U-Net architecture to detect the TC wind structure, including maximum wind speed area and hurricane-force wind speed area, in the outputs of the convective-scale AROME model. A dataset of 400 AROME forecasts over the West Indies domain has been entirely hand-labeled by experts, following a rigorous process to reduce heterogeneities. The U-Net performs well on a wide variety of TC intensities and shapes, with an average intersection-over-union metric of around 0.8. Its performances, however, strongly depend on the TC strength, and the detection of weak cyclones is more challenging since their structure is less well defined. The U-Net also significantly outperforms an operational heuristic detection model, with a significant gain for weak TCs, while running much faster. In the last part, the capacity of the U-Net to generalize on slightly different data is demonstrated in the context of a domain change and a resolution increase. In both cases, the pretrained U-Net achieves similar performances as the original dataset.

Differences in Discounting Behavior and Brain Responses for Food and Money Reward.

Most neuroeconomic research seeks to understand how value influences decision-making. The influence of reward type is less well understood. We used functional magnetic resonance imaging (fMRI) to investigate delay discounting of primary (i.e., food) and secondary rewards (i.e., money) in 28 healthy, normal-weighted participants (mean age = 26.77; 18 females). To decipher differences in discounting behavior between reward types, we compared how well-different option-based statistical models (exponential, hyperbolic discounting) and attribute-wise heuristic choice models (intertemporal choice heuristic, dual reasoning and implicit framework theory, trade-off model) captured the reward-specific discounting behavior. Contrary to our hypothesis of different strategies for different rewards, we observed comparable discounting behavior for money and food (i.e., exponential discounting). Higher k values for food discounting suggest that individuals decide more impulsive if confronted with food. The fMRI revealed that money discounting was associated with enhanced activity in the right dorsolateral prefrontal cortex, involved in executive control; the right dorsal striatum, associated with reward processing; and the left hippocampus, involved in memory encoding/retrieval. Food discounting, instead, was associated with higher activity in the left temporoparietal junction suggesting social reinforcement of food decisions. Although our findings do not confirm our hypothesis of different discounting strategies for different reward types, they are in line with the notion that reward types have a significant influence on impulsivity with primary rewards leading to more impulsive choices.

HEURISTIC MODEL FOR ASSESSING DISRUPTIONS IN THE SYNCHRONIZATION OF AIRBAG DEPLOYMENT WITH THE DRIVER'S BODY DURING A COLLISION, BASED ON REPAIRED LONGITUDINAL BEAMS

The article presents a heuristic model used to evaluate the level of disorder in the synchronization of airbag operation with a moving phantom head during a frontal collision caused by a change in the longitudinal stiffness. The article describes three areas of research. The first presents the results of the strength tests of longitudinal models: reference and welded joint. Based on these, the relative stiffness decrease coefficient (SDC) of the longitudinal was determined. The second presents an analytical model of the airbag activation algorithm and the impact of SDC on its operation. The third determined the contact time of the phantom head with the airbag, taking into account the SDC. Based on the selected case study, a spatiotemporal analysis was conducted, indicating the level of disorder in the synchronization of the airbag with the moving phantom head due to the change in longitudinal stiffness.

Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings.