Many actions have instrumental aims, in which we move our bodies to achieve a physical outcome in the environment. However, we also perform actions with epistemic aims, in which we move our bodies to acquire information and learn about the world. A large literature on action recognition investigates how observers represent and understand the former class of actions; but what about the latter class? Can one person tell, just by observing another person’s movements, what they are trying to learn? Here, (...) 5 experiments explore epistemic action understanding. We filmed volunteers playing a ‘physics game’ consisting of two rounds: Players shook an opaque box and attempted to determine (i) the number of objects hidden inside, or (ii) the shape of the objects inside. Then, independent subjects watched these videos and were asked to determine which videos came from which round: Who was shaking for number and who was shaking for shape? Across several variations, observers successfully determined what an actor was trying to learn, based only on their actions (i.e., how they shook the box)—even when the box’s contents were identical across rounds. These results demonstrate that humans can infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding. (shrink)

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium (...) traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole. (shrink)

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into multiple clusters, (...) including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)

Security issue against different attacks is the core topic of cyberphysical systems. In this paper, optimal control theory, reinforcement learning, and neural networks are integrated to provide a brief overview of optimal robust control strategies for a benchmark power system. First, the benchmark power system models with actuator and sensor attacks are considered. Second, we investigate the optimal control issue for the nominal system and review the state-of-the-art RL methods along with the NN implementation. Third, we propose several robust control (...) strategies for different types of cyberphysical attacks via the optimal control design, and stability proofs are derived through Lyapunov theory. Furthermore, the stability analysis with the NN approximation error, which is rarely discussed in the previous works, is studied in this paper. Finally, two different simulation examples demonstrate the effectiveness of our proposed methods. (shrink)

Abusive supervision has long been found to have remarkably negative impacts on individual and organizational outcomes. Accordingly, prior studies have explored many organizational and supervisory predictors of abusive supervision and offered several interventions to reduce it. However, extant research lacks the bottom-up perspective to explore how employees can act to reduce abusive supervision, which is an important factor that enriches abusive supervision literature and helps employees protect themselves from being abused. Drawing on self-disclosure theory, we develop a model of whether (...) and how employee boundary blurring behavior may protect them from being abused by their supervisors. Specifically, we conducted two studies to test the theoretical model, including a scenario-based experimental study and a multi-source, multi-wave field study. The results reveal a negative indirect effect of employee boundary blurring behavior on abusive supervision via supervisor liking toward the employee. By uncovering employee boundary blurring behavior as an antecedent of abusive supervision, we enrich the abusive supervision literature with a bottom-up behavioral strategy for employees to proactively protect themselves from being abused. We hope our findings will encourage future studies to identify boundary conditions and other solutions for employees to minimize the risk of being abused. (shrink)

In practical application studies of glider formations, ocean currents are a major influencing factor in their path planning. The purpose of this paper is to solve the path planning problem of glider formations in time-varying ocean currents and establish gliders, glider formation, and ocean current models based on existing data. The Doc-CNN architecture is tailored to conform to the operation and environment characteristics of gliders in practical application. After experiments, the algorithm of the improved architecture can be used in the (...) path planning task of glider formation. The algorithm architecture is compared and tested on two datasets, grid maps and ocean maps. Doc-CNN has an advantage over architecture without being tailored to glider characteristics and makes full use of known global information and local information collected by gliders themselves. The results show that the path planning problem of glider formation in ocean currents can be solved by using Doc-CNN. (shrink)

Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus (...) line capacity scheduling algorithm, and display them in an intuitive way. The experimental results show that the algorithm model established in this paper can basically meet the data requirements of operation and management, and can provide decision support for customized bus line planning. (shrink)

In parallel with the rapid rise in breast cancer incidence, there is also a noticeable rise in the number of patients who experience persistent negative body image cognition after breast cancer surgery. This study aimed to explore the differences in illness perception, social support, and body image cognition among breast cancer patients with different surgical methods, and the correlation, regression, and mediation among the three variables. The Brief Illness Perception Questionnaire, the Social Support Rating Scale and the Body Image Cognition (...) after Breast Cancer Questionnaire were used to evaluate breast cancer patients’ illness perception, social support and body image cognition. Data analyses were performed by descriptive statistics, independent samples t-test, analysis of variance, Pearson correlation, and linear regression. The mediation was explored by the PROCESS V3.3. The study found that breast cancer patients undergoing radical mastectomy and modified radical mastectomy demonstrated more negative illness perception, body image cognition, and lower social support compared with the patients receiving nipple-sparing mastectomy. The subscale cognitive representation of BIPQ was strongly positively correlated with BIBCQ. Illness perception positively predicted body image cognition, while social support negatively predicted body image cognition. Social support partially mediated illness perception and body image cognition, exhibiting a positive role in post-operative body image cognition. (shrink)

本书主要探讨传统思想与科学发展之间的关系,涉及传统哲学的科学基础、科学发展的思想原因、科学思想的涵义和演变、以及传统思想对数学、医学等各门学科的作用和影响。.

本书主要探讨传统思想与科学发展之间的关系,涉及传统哲学的科学基础、科学发展的思想原因、科学思想的涵义和演变、以及传统思想对数学、医学等各门学科的作用和影响。.