A84: Factors Influencing Parents\u27 Intention to Involve Autistic Preschoolers in Outdoor Physical Activities

Purpose: Outdoor physical activities (OPA) can not only improve the physical fitness and health of autistic preschool children, but also provide an indispensable opportunity for their social learning and interaction. However, parents’ intentions of providing OPA for their autistic children are complex due to reasons such as potential risk. Theory of Planned Behavior（TPB）has conceptualized that attitude (AT), subjective norms (SN), and perceived behavioral control (PBC) are influential factors of behavioral intention. Perceived risk and perceived usefulness are suggested to associate with one’s intention, therefore, the purpose of this study was to test an extended TPB model on parent’s intention of providing OPA among parents of autistic children. Methods: This was a cross-sectional study. A convenient sample of parents of 301 autistic preschoolers (mean age = 3.45±1.10 years old; boys=219; girls = 68) were recruited. Parents reported to questionnaires assessing their AT, SN, PBC, and intention (Ajzen, 1991; Yang, 2017) as well as perceived risk and perceived usefulness of providing OPA for their autistic children (Davis et al., 1989; Yu, 2016). Structural Equation Modeling (SEM) using SmartPLS software was employed to investigate the extended TPB model by including perceived risk and perceived usefulness (exogenous variables) with AT, SN, PBC to synergistically predict intention (endogenous variable). Results: The results revealed that autistic preschooler parents’ perceived usefulness (p \u3c 0.01, f2= 0.087）had a significant and positive prediction on intention in addition to the significant contributions from AT (p \u3c 0.01, f2=0.053), SN (p \u3c 0.01, f2=0.047), and PBC (p \u3c 0.01, f2=0.060). However, perceived risk (p \u3c 0.01, f2= 0.070) had a significant and negative prediction on intention. Besides, perceived usefulness (p \u3c 0.01, f2= 0.108) was positively correlated with AT and AT partially mediated the association between perceived usefulness and intention (Variance Accounted For= 23.2%). In general, perceived usefulness, PBC, AT, SN and perceived risk had a high explanatory power to intention (R2=0.536). Conclusion: The extended TPB model demonstrated to be a useful framework to explain autistic preschooler parents’ intention of providing OPA for their autistic children. Perceived risk and perceived usefulness are critical to parents’ intention in addition to PBC, AT and SN, which should also be the targets of intervention in practice. This finding also suggests practitioners may help parents’ understand the usefulness of OPA to help autistic preschoolers’ parents form positive attitude of OPA, then boosting their intention to engage their autistic children in OPA

Ordonnancement cyclique robuste appliqué à la gestion des conteneurs dans les ports maritimes de taille moyenne

This PhD thesis is dedicated to propose a robust cyclic scheduling methodology applied to container management of medium sized seaport which faces ever changing terminal conditions and the limited predictability of future events and their timing. The robust cyclic scheduling can be seen not just a predictable scheduling to compute a container transportation schedule, but also a reactive scheduling to eliminate the disturbances in real time. In this work, the automated intelligent vehicles (AIV) are used to transport the containers, and the P-time strongly connected event graph (PTSCEG) is used as a graphical tool to model the container transit procedures. Before the arrival of the container vessel, a cyclic container transit schedule can be given by the mixed integer programming (MIP) method in short time. The robustness margins on the nodes of the system can be computed by robustness algorithms in polynomial computing time. After the stevedoring begins, this robust cyclic schedule is used. When a disturbance is observed in system, it should be compared with the known robustness margin. If the disturbance belongs to the robustness margin, the robustness algorithm is used to eliminate the disturbance in a few cycle times. If not, the MIP method is used to compute a new cyclic schedule in short timeCette thèse présente une méthodologie d’ordonnancement cyclique robuste appliquée à la gestion des conteneurs dans les ports maritimes de taille moyenne. Ces derniers sont sujet constamment à des variations des conditions des terminaux, la visibilité réduite sur des évènements futurs ne permet pas de proposer une planification précise des tâches à accomplir. L’ordonnancement cyclique robuste peut jouer un rôle primordial. Il permettra non seulement de proposer un ordonnancement prédictif pour le transport des conteneurs, mais aussi, il proposera également une planification robuste permettant d’éliminer les perturbations éventuelles en temps réel. Dans ce travail nous utilisons les Véhicules Intelligents Automatisés (AIV) pour transporter les conteneurs et nous modélisons les procédures de transit de ces derniers par des graphes d’évènements P-temporels fortement connexes (PTSCEG). Avant l’arrivée d’un porte conteneur au port, un plan (planning) de transport des conteneurs est proposé en un temps court par la programmation linéaire mixte (MIP). Des algorithmes polynomiaux de calcul de robustesse permettent de calculer sur les différents nœuds du système les marges de robustesse. Une fois le navire à quai, l’ordonnancement cyclique robuste est appliqué. Lorsqu’une perturbation est observée (localisée) dans le système, une comparaison avec la marge de robustesse connue est effectuée. Si cette perturbation est incluse dans la marge de robustesse, l’algorithme robuste est utilisé pour éliminer ces perturbations en quelques cycles. Dans le cas où la perturbation est trop importante, la méthode MIP est utilisée pour calculer un nouvel ordonnancement cyclique en un temps rédui

An OLS and GMM Combined Algorithm for Text Analysis for Heterogeneous Impact in Online Health Communities

The increase of doctors\u27 activity in online health communities (OHCs) plays a decisive role in their development. Although the literature on the determinants of doctors\u27 online activities has received considerable attention, the impact of illness severity on these factors remains rare. A network externality analytical framework is constructed to explain the factors (that is, responsiveness, involvement, word-of-mouth, incentives, price, titles and gender) affecting online doctors\u27 behavior, and assess whether factors differ by. By developing text analysis of 4916 doctors\u27 data from a Chinese OHC, this paper applies ordinary least squares (OLS) and General Method of Moments (GMM) to analyze whether the determinants are equal across serious, moderate, and mild illnesses. Our experiment results find that the determinants affecting doctors\u27 online service activity substantially differ across illness severity. Experiments prove the effectiveness of the proposed OLS and GMM methods and demonstrate that they are applicable in online medical field

Supported Trust Region Optimization for Offline Reinforcement Learning

Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error. Most policy constraint methods regularize the density of the trained policy towards the behavior policy, which is too restrictive in most cases. We propose Supported Trust Region optimization (STR) which performs trust region policy optimization with the policy constrained within the support of the behavior policy, enjoying the less restrictive support constraint. We show that, when assuming no approximation and sampling error, STR guarantees strict policy improvement until convergence to the optimal support-constrained policy in the dataset. Further with both errors incorporated, STR still guarantees safe policy improvement for each step. Empirical results validate the theory of STR and demonstrate its state-of-the-art performance on MuJoCo locomotion domains and much more challenging AntMaze domains.Comment: Accepted at ICML 202

Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning

Offline multi-agent reinforcement learning is challenging due to the coupling effect of both distribution shift issue common in offline setting and the high dimension issue common in multi-agent setting, making the action out-of-distribution (OOD) and value overestimation phenomenon excessively severe. Tomitigate this problem, we propose a novel multi-agent offline RL algorithm, named CounterFactual Conservative Q-Learning (CFCQL) to conduct conservative value estimation. Rather than regarding all the agents as a high dimensional single one and directly applying single agent methods to it, CFCQL calculates conservative regularization for each agent separately in a counterfactual way and then linearly combines them to realize an overall conservative value estimation. We prove that it still enjoys the underestimation property and the performance guarantee as those single agent conservative methods do, but the induced regularization and safe policy improvement bound are independent of the agent number, which is therefore theoretically superior to the direct treatment referred to above, especially when the agent number is large. We further conduct experiments on four environments including both discrete and continuous action settings on both existing and our man-made datasets, demonstrating that CFCQL outperforms existing methods on most datasets and even with a remarkable margin on some of them.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Maximum Wind Power Tracking of Doubly Fed Wind Turbine System Based on Adaptive Gain Second-Order Sliding Mode

This paper proposes an adaptive gain second-order sliding mode control strategy to track optimal electromagnetic torque and regulate reactive power of doubly fed wind turbine system. Firstly, wind turbine aerodynamic characteristics and doubly fed induction generator (DFIG) modeling are presented. Then, electromagnetic torque error and reactive power error are chosen as sliding variables, and fixed gain super-twisting sliding mode control scheme is designed. Considering that uncertainty upper bound is unknown and is hard to be estimated in actual doubly fed wind turbine system, a gain scheduled law is proposed to compel control parameters variation according to uncertainty upper bound real-time. Adaptive gain second-order sliding mode rotor voltage control method is constructed in detail and finite time stability of doubly fed wind turbine control system is strictly proved. The superiority and robustness of the proposed control scheme are finally evaluated on a 1.5 MW DFIG wind turbine system

Effect of Surface Microstructures on Hydrophobicity and Barrier Property of Anticorrosive Coatings Prepared by Soft Lithography

Enhancing the hydrophobicity of organic coatings retards their interaction with water and often leads to better protectiveness over metal corrosion. In this study, a soft lithography method was used to prepare epoxy coatings which showed surface microstructures in high replication to sandpapers. The effect of microstructures on coating’s hydrophobicity and barrier property was investigated. Compared to flat coatings, the microstructured coatings showed much higher water contact angles, which further increased with finer sandpapers. Determined by electrochemical impedance spectroscopy (EIS), the flat coating exhibited a higher anticorrosive performance than the microstructured coatings. With the use of finer sandpaper, the groove size of the corresponding microstructured coating was reduced. And a lower anticorrosive performance was observed since more defects might be formed in a given area of coating during the imprinting process. As the groove size of the coatings was further decreased to 5.7 µm, the microstructures became too small for water to easily penetrate through. Therefore, trapped air acted as an additional barrier and contributed to an increased anticorrosive performance compared to other microstructured coatings