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By virtue of the advances in sensing techniques, finite element (FE) model updating (FEMU) using static and dynamic data has been recently employed to improve identification on updating parameters. Using heterogeneous data can provide useful information to improve parameter identifiability in FEMU. It is worth noting that the useful information from the heterogeneous data may be diluted in the conventional FEM framework. The conventional FEMU framework in previous studies have used heterogeneous data at once to compute residuals in the objective function, and they are condensed to be a scalar. In this implementation, it should be careful to formulate the objective function with proper weighting factors to consider the scale of measurement and relative significances. Otherwise, the information from heterogeneous data cannot be efficiently utilized. For FEMU of the bridge, parameter compensation may exist due to mutual dependence among updating parameters. This aggravates the parameter identifiability to make the results of the FEMU worse. To address the limitation of the conventional FEMU method, this study proposes a sequential framework for the FEMU of existing bridges. The proposed FEMU method uses two steps to utilize static and dynamic data in a sequential manner. By using them separately, the influence of the parameter compensation can be suppressed. The proposed FEMU method is verified through numerical and experimental study. Through these verifications, the limitation of the conventional FEMU method is investigated in terms of parameter identifiability and predictive performance. The proposed FEMU method shows much smaller variabilities in the updating parameters than the conventional one by providing the better predictions than those of the conventional one in calibration and validation data. Based on numerical and experimental study, the proposed FEMU method can improve the parameter identifiability using the heterogeneous data and it seems to be promising and efficient framework for FEMU of the existing bridge.

In the hotel booking market, high click-through rates are essential for online travel agencies (OTAs) to earn commissions. Given the dominance of mobile devices in web traffic, analyzing the mobile click-through decision-making process plays a vital role in search engine optimization. This study proposes a sequential framework that leverages Bayesian inference to model individual users’ click-through behaviors using user digital footprints, which encompass sequences of search, browse, compare, and click-through actions. This framework extracts three categories of information based on the degrees of dynamism in the hotel search process, ranging from less dynamic to highly dynamic levels: static hotel attributes, information cues in the search results, and temporal characteristics of user behaviors. Extensive experiments on a global OTA mobile clickstream dataset with over 600,000 observations reveal the substantial superiority of the proposed framework over the baseline models like probit regression and Naive Bayes. Notably, temporal characteristics emerge as the most important category. Drawing on our model, we delve into the interpretability of these three information categories. Additionally, we compare their varying impacts across different devices. Beyond these findings, this study offers valuable managerial implications for mobile OTA search engine marketing and optimization.

An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB 2.0), which brings together known models from cognitive psychology, cognitive neuroscience, and motor learning. This processing framework accounts for the many different behavioral results obtained with the DSP task and unveils important properties of the cognitive system. C-SMB 2.0 assumes that a versatile central processor (CP) develops multimodal, central-symbolic representations of short motor segments by repeatedly storing the elements of these segments in short-term memory (STM). Independently, the repeated processing by modality-specific perceptual and motor processors (PPs and MPs) and by the CP when executing sequences gradually associates successively used representations at each processing level. The high dependency of these representations on active context information allows for the rapid serial activation of the sequence elements as well as for the executive control of tasks as a whole. Speculations are eventually offered as to how the various cognitive processes could plausibly find their neural underpinnings within the intricate networks of the brain.

Along with global climate change, more frequent extreme climate phenomena have led to an increasing number of and increasingly severe flood disasters. Extreme rainfall events are capable of generating substantial amounts of surface runoff. Concurrently, the progression of urbanization has given rise to the expansion of impervious surfaces, thereby augmenting the likelihood of flood disasters. As China’s most flood-vulnerable region, the Sichuan Basin has sustained recurrent catastrophic flooding throughout history. This study establishes three policy-relevant hotspots across the basin as critical testbeds to quantify climate-driven changes in flood recurrence intervals. Utilizing CMIP6 projections under different SSPs scenarios, we developed a physics-informed process-based modeling framework integrating statistical downscaling, adjustments for extreme values and flood frequency analysis, specifically addressing how anthropogenically modified hydrological regimes amplify extreme event probabilities in this monsoon-dominated basin. The findings suggest that by the conclusion of the current century, the study area is likely to experience a notable increase in temperature, with an anticipated rise of approximately 1.7 ℃, and an intensification in precipitation, with an increase of 9.4% percent. Furthermore, the likelihood of extreme flood disaster events is projected to double, underscoring the imperative for robust climate adaptation and disaster mitigation strategies.

Multi-object tracking (MOT), especially by using a moving monocular camera, is a very challenging task in the field of visual object tracking. To tackle this problem, the traditional tracking-by-detection-based method is heavily dependent on detection results. Occlusion and mis-detections will often lead to tracklets or drifting. In this paper, the tasks of MOT and camera motion estimation are formulated as finding a maximum a posteriori (MAP) solution of joint probability and synchronously solved in a unified framework. To improve performance, we incorporate the three-dimensional (3D) relative-motion model into a sequential Bayesian framework to track multiple objects and the camera’s ego-motion estimation. A 3D relative-motion model that describes spatial relations among objects is exploited for predicting object states robustly and recovering objects when occlusion and mis-detections occur. Reversible jump Markov chain Monte Carlo (RJMCMC) particle filtering is applied to solve the posteriori estimation problem. Both quantitative and qualitative experiments with benchmark datasets and video collected on campus were conducted, which confirms that the proposed method is outperformed in many evaluation metrics.

A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes a robust scale adaptive tracking algorithm to predict target scale by a sequential Monte Carlo method and determine the target location by the correlation filter simultaneously. By analyzing the response map of the target region, the completeness of the target can be measured by the peak-to-sidelobe rate (PSR), i.e., the lower the PSR, the more likely the target is being occluded. A strict template update strategy is designed to accommodate the appearance change and avoid template corruption. If the occlusion occurs, a retained scheme is allowed and the tracker refrains from drifting away. Additionally, the feature integration is incorporated to guarantee the robustness of the proposed approach. The experimental results show that our method outperforms other state-of-the-art trackers in terms of both the distance precision and overlap precision on the publicly available TB-50 dataset.

The aim of this paper is to identify the main drivers of highly skilled migration between regions. We argue that the spatial mobility of individuals should not be considered in terms of one-off displacements, but rather as a sequence of migration decisions within a certain time period. The important context of the research is provided by the economic transformation of Poland, accompanied by the growing demand for education, and the lack of well-established patterns of graduate mobility. By applying multinomial logit modelling on a unique database of Polish graduates, we find that all the tested migration strategies can be explained in terms of structural factors, human capital characteristics or aspirations/capabilities related variables.

We discuss the question of which features and/or properties make a method for solving a given problem belong to the “Newtonian class.” Is it the strategy of linearization (or perhaps, second-order approximation) of the problem data (maybe only part of the problem data)? Or is it fast local convergence of the method under natural assumptions and at a reasonable computational cost of its iteration? We consider both points of view, and also how they relate to each other. In particular, we discuss abstract Newtonian frameworks for generalized equations, and how a number of important algorithms for constrained optimization can be related to them by introducing structured perturbations to the basic Newton iteration. This gives useful tools for local convergence and rate-of-convergence analysis of various algorithms from unified perspectives, often yielding sharper results than provided by other approaches. Specific constrained optimization algorithms, which can be conveniently analyzed within perturbed Newtonian frameworks, include the sequential quadratic programming method and its various modifications (truncated, augmented Lagrangian, composite step, stabilized, and equipped with second-order corrections), the linearly constrained Lagrangian methods, inexact restoration, sequential quadratically constrained quadratic programming, and certain interior feasible directions methods. We recall most of those algorithms as examples to illustrate the underlying viewpoint. We also discuss how the main ideas of this approach go beyond clearly Newton-related methods and are applicable, for example, to the augmented Lagrangian algorithm (also known as the method of multipliers), which is in principle not of Newton type since its iterations do not approximate any part of the problem data.

This chapter contains sections titled: Introduction Case Studies: Bob and Leon Discussion Conclusion References

This chapter contains sections titled: Introduction Policies and Policy Processes Planning and Implementation Economic Appraisals Summary and Learning Points References