“My favorite quote is “Chance favors only the prepared mind” from Louis Pasteur … The biggest change in my scientific working environment in the past 10 years has been to move to a different country ...and to have two kids …” Find out more about M. Ángeles (Tati) Fernández‐Ibáñez in her Introducing … Profile.
Responses to rising patient demand, financial constraints, and political dogma in the UK may be well intended and focus on patient need, but they fail to respect the human nature of the professional ...groups on whom we rely to deliver care. Senior doctors retire early to avoid pressures and financial penalties on their pensions resulting from continued employment.
El objetivo es analizar las condiciones laborales objetivas y subjetivas de los empleados de los alojamientos urbanos de Villavicencio. Metodológicamente, se siguió un enfoque cuantitativo con diseño ...no experimental, descriptivo y correlacional. La información se obtuvo de 142 empleados de hoteles inscritos en el Registro Nacional de Turismo en Villavicencio, mediante el instrumento CV GOHISALO. El análisis contempla la multidimensionalidad laboral objetiva y subjetiva y la ponderación de variables objetivas. Los resultados muestran un Índice de la Calidad del Empleo Objetivo promedio de 57,49 puntos, sin alcanzar un nivel aceptable. El salario es la variable que presenta la mayor insatisfacción, luego está la administración del tiempo libre y desarrollo personal. Se concluye que las empresas hoteleras de Villavicencio posiblemente se han concentrado en estrategias para la productividad, competitividad y el crecimiento económico, descuidando la CVL y la desmotivación en el trabajador al descuidar su integridad psicológica, humana, familiar y social.
Markus Stöckl
Angewandte Chemie,
December 12, 2022, Volume:
61, Issue:
50
Journal Article
Peer reviewed
Open access
“My group has fun by pulling me into a competition who makes the best cake … My biggest motivation is to keep an working environment where all workers can unfold their capabilities …” Find out more ...about Markus Stöckl in his Introducing … Profile.
Andrew C.‐H. Sue
Angewandte Chemie International Edition,
August 1, 2022, Volume:
61, Issue:
31
Journal Article
Peer reviewed
“The biggest change in my scientific working environment in the past 10 years has been moving to China, a country that I never got chance to explore in the past … I chose chemistry as a career ...because I liked playing with LEGO® in my childhood. Now I'm just doing the same thing on the molecular level …” Find out more about Andrew Sue in his Introducing … Profile.
Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, ...domain label, and data structure, are essential to bridging the labeled source domain and unlabeled target domain. However, most existing UDA-based methods use only the former two information and ignore the modeling of data structure, which make the information contained in the features extracted by the deep network incomplete. To tackle this issue, a domain adversarial graph convolutional network (DAGCN) is proposed to model the three types of information in a unified deep network and achieving UDA. The first two types of information are modeled by the classifier and the domain discriminator, respectively. In data structure modeling, a convolutional neural network (CNN) is first employed to exact features from input signals. After that, the CNN features are input to the proposed graph generation layer to construct instance graphs by mining the relationship of structural characteristics of samples. Then, the instance graphs are modeled by a graph convolutional network, and the maximum mean discrepancy metric is leveraged to estimate the structure discrepancy of instance graphs from different domains. Experimental results conducted on two case studies demonstrate that the proposed DAGCN can not only obtain the best performance among the comparison methods, but also can extract transferable features for domain adaptation. The code library is available at: https://github.com/HazeDT/DAGCN .