In recent years, the outbreak and persistence of COVID-19 has greatly changed the way people work, and encouraging employees to work online from home has become a new form of work for organizations ...responding to the epidemic. Based on the W-HR model, this paper explored supervisor support as a situational resource in the context of online office, aiming to verify the changes in work-family status caused by individuals facing the background of supervisor support, and then relate employees’ proactive behavior. Meanwhile, the predicted moderating effect of job role identification on supervisor support and work-family conflict was verified by considering the role identification generated in the positive interaction between employees and supervisors as individual resources. In this study, 555 employees from enterprises in the provinces of Jiangsu and Guangdong were selected as the research participants, and data were recovered at two time nodes. The results show that: (1) Perceived supervisor support significantly relates employee proactive behavior. (2) Work-family conflict mediates the relationship between perceived supervisor support and employee proactive behavior. (3) Job role identification has a significant moderating effect on the relationship between perceived supervisor support and work-family conflict, and high level of job role identification moderated the mediating effect of work-family conflict on perceived supervisor support and employee proactive behavior significantly. This paper expands the research perspective of employee proactive behavior. It can be used as a reference for enterprises to formulate strategies to improve employee proactive behavior in the process of digital reform.
Based on a literature review and questionnaire survey, the study proposes two tendencies of employees’ status pursuit motivation. It applies exploratory factor analysis, confirmatory factor analysis, ...and other statistical methods to develop a scale of motivation for status which contains nine items and two dimensions. The development stage of this scale consists of the generation of the initial scale, exploratory research and verification research. In the generation stage of the initial scale, we obtained 12 initial questions of the status scale by combing status literature and adopting open questionnaire among employees of knowledge-based enterprises, and determined 10 of them as the questions of the initial scale by text analysis method. In the exploratory research stage, we conducted pre-survey and formal questionnaire collection on some enterprises in Guangdong and Jiangsu provinces of China. In the pre-survey stage, we distributed 100 questionnaires (81 were recovered), and the pre-survey results were satisfactory. Accordingly, 400 questionnaires were distributed (370 were recovered). In the exploratory research stage, we verified the organization member status pursuit motivation scale consisting of two dimensions and nine items. In the stage of validation research, 450 questionnaires were distributed to knowledge-intensive enterprises in Guangdong province, Jiangsu Province, Jiangxi Province and so on (425 were returned). The validation research on organizational membership motivation scale included violation estimation test, confirmatory factor analysis, model fit evaluation, reliability and validity test. Finally, a scale of status pursuit motivation of organization members consisting of two dimensions and nine items is obtained. This study expands the measurement methods of status research from a new perspective and lays a foundation for subsequent empirical research on organizational member status pursuit motivation.
Let $\mathcal{S}$ be a Serre class in the category of modules and $\mathfrak{a}$ an ideal of a commutative Noetherian ring $R$. We study the containment of Tor modules, Koszul homology and local ...homology in $\mathcal{S}$ from below. With these results at our disposal, by specializing the Serre class to be Noetherian or zero, a handful of conclusions on Noetherianness and vanishing of the foregoing homology theories are obtained. We also determine when $\mathrm{Tor}_{s+t}^R(R/\mathfrak{a},X)\cong\mathrm{Tor}_{s}^R(R/\mathfrak{a},\mathrm{H}_{t}^\mathfrak{a}(X))$. KCI Citation Count: 0
Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for ...instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. The results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.
Deep learning of vortex-induced vibrations Raissi, Maziar; Wang, Zhicheng; Triantafyllou, Michael S. ...
Journal of fluid mechanics,
02/2019, Letnik:
861
Journal Article
Recenzirano
Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the ...structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier–Stokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space–time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space–time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.
Due to their outstanding overall performances, carbon fiber/poly(ether-ether-ketone) (CF/PEEK) composites have attracted a lot of interest recently. High-performance CF/PEEK composites have many ...advantages such as high strength, good toughness, and high service temperature, which have been widely used in various high-precision fields. This paper reviews the research progress of the CF/PEEK composite molding process and its applications in view of the research hotspots in recent years, laying the foundation for the research on the preparation technology and industrial application of the material. CF/PEEK molding process mainly includes injection molding, press molding, filament winding, 3D Printing, and automated fiber placement (AFP). Different processing methods and material ratios result in CF/PEEK materials with different mechanical properties. Since CF/PEEK has its own unique advantages over traditional metal materials in terms of mechanical properties, corrosion resistance, and density, CF/PEEK materials can be used to replace metal materials in many applications, such as aviation and aerospace, biomedical field and automotive part.
We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies ...for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.
Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy ...data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving
inverse flow problems
is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review
flow physics-informed learning
, integrating seamlessly data and mathematical models, and implement them using physics-informed neural networks (PINNs). We demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows, supersonic flows, and biomedical flows.
Graphical abstract
The topic of multi-person pose estimation has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, ...such as occluded keypoints, invisible keypoints and complex background, which cannot be well addressed. In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints. More specifically, our algorithm includes two stages: GlobalNet and RefineNet. GlobalNet is a feature pyramid network which can successfully localize the "simple" keypoints like eyes and hands but may fail to precisely recognize the occluded or invisible keypoints. Our RefineNet tries explicitly handling the "hard" keypoints by integrating all levels of feature representations from the GlobalNet together with an online hard keypoint mining loss. In general, to address the multi-person pose estimation problem, a top-down pipeline is adopted to first generate a set of human bounding boxes based on a detector, followed by our CPN for keypoint localization in each human bounding box. Based on the proposed algorithm, we achieve state-of-art results on the COCO keypoint benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1 on the COCO test-challenge dataset, which is a 19% relative improvement compared with 60.5 from the COCO 2016 keypoint challenge. Code1 and the detection results for person used will be publicly available for further research.
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