scholarly journals An Algorithmic Framework for Positive Action

2021 ◽  
Author(s):  
Oliver Thomas ◽  
Miri Zilka ◽  
Adrian Weller ◽  
Novi Quadrianto
Keyword(s):  
Positive Action ◽  
Algorithmic Framework

An empirical study on factors influencing level of employee engagement with reference to select organization

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 109-114
Author(s):  
Dr. Srikrishna Gade ◽  
Lavanya. K
Keyword(s):  
Human Resources ◽  
Employee Engagement ◽  
Employee Development ◽  
Management Theory ◽  
Work Place ◽  
Development Programs ◽  
Great Work ◽  
Factors Influencing ◽  
Positive Action ◽  
Efficiency And Productivity

There is no exact definition for the term Employee engagement yet. The term Employee engagement means that the employee feel the belongingness towards the organization always strives to the growth of their organization. An Engaged employee means one who fully enthusiastic about their work and takes positive action for organizations reputation and interests. Employee engagement first appeared as a concept in management theory in 1990s. Employee engagement practices are well established in the management of human resources. An organization with high employee engagement might have higher productivity than the organizations having less employee engagement level employees. Whereas employee engagement is directly proportional to the organizations productivity as higher the engagement level of employee results higher efficiency and productivity. Also the employee engagement may directly or indirectly relate to the job satisfaction or morale of employee. By understanding the importance of employee engagement many organizations are doing engagement practices such as providing great work place culture, employee development programs to enhance the engagement level of employee to raise productivity and daily performances.

Domains of Socialization

2019 ◽  
pp. 72-90
Author(s):  
Joan E. Grusec
Keyword(s):  
Young Adults ◽  
Zone Of Proximal Development ◽  
Proximal Development ◽  
Group Participation ◽  
Guided Learning ◽  
Positive Action ◽  
Affect And Cognition ◽  
And Control ◽  
Value Learning ◽  
Over Time

This chapter surveys how behavior, affect, and cognition with respect to parenting and moral development have been conceptualized over time. It moves to a discussion of domains of socialization; that is, different contexts in which socialization occurs and where different mechanisms operate. Domains include protection where the child is experiencing negative affect, reciprocity where there is an exchange of favors, group participation or learning through observing others and engaging with them in positive action, guided learning where values are taught in the child’s zone of proximal development, and control where values are learned through discipline and reward. Research using narratives of young adults about value-learning events suggests that inhibition of antisocial behavior is more likely learned in the control domain, and prosocial behavior more likely in the group participation domain. Internalization of values, measured by narrative meaningfulness, is most likely in the group participation domain.

An Algorithmic Framework for Performing Collaborative Filtering

2017 ◽  
Vol 51 (2) ◽  
pp. 227-234 ◽  
Author(s):  
Jonathan L. Herlocker ◽  
Joseph A. Konstan ◽  
Al Borchers ◽  
John Riedl
Keyword(s):  
Collaborative Filtering ◽  
Algorithmic Framework

scholarly journals Climate Change Inaction and Optimism

Philosophies ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 61
Author(s):  
Philip J. Wilson
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Collective Action ◽  
Public Discourse ◽  
Collective Action Problem ◽  
The Public ◽  
Policy Context ◽  
Positive Action ◽  
Ideological Fantasy

The problem of climate change inaction is sometimes said to be ‘wicked’, or essentially insoluble, and it has also been seen as a collective action problem, which is correct but inconsequential. In the absence of progress, much is made of various frailties of the public, hence the need for an optimistic tone in public discourse to overcome fatalism and encourage positive action. This argument is immaterial without meaningful action in the first place, and to favour what amounts to the suppression of truth over intellectual openness is in any case disreputable. ‘Optimism’ is also vexed in this context, often having been opposed to the sombre mood of environmentalists by advocates of economic growth. The greater mental impediments are ideological fantasy, which is blind to the contradictions in public discourse, and the misapprehension that if optimism is appropriate in one social or policy context it must be appropriate in others. Optimism, far from spurring climate change action, fosters inaction.

scholarly journals Prioritising surgical cases deferred by the COVID-19 pandemic: an ethics-inspired algorithmic framework for health leaders

BMJ Leader ◽  
2021 ◽  
pp. leader-2020-000343
Author(s):  
Amit Jain ◽  
Tinglong Dai ◽  
Christopher G Myers ◽  
Punya Jain ◽  
Shruti Aggarwal
Keyword(s):  
Decision Analysis ◽  
Health System ◽  
System Capacity ◽  
Surgical Case ◽  
The World ◽  
The Face ◽  
Algorithmic Framework ◽  
Limited Health ◽  
Healthcare Leaders

Elective surgical suspension during the COVID-19 pandemic resulted in a sizeable surgical case backlog throughout the world. As we ramp back up, how do we decide which cases take priority? Potential future waves (or a future pandemic) may lead to additional surgical shutdown and subsequent reopening. Deciding which cases to prioritise in the face of limited health system capacity has emerged as a new challenge for healthcare leaders. Here we present an ethically grounded and operationally efficient surgical prioritisation framework for healthcare leaders and practitioners, drawing insights from decision analysis and organisational sciences.

scholarly journals Two-step machine learning enables optimized nanoparticle synthesis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Flore Mekki-Berrada ◽  
Zekun Ren ◽  
Tan Huang ◽  
Wai Kuan Wong ◽  
Fang Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Properties ◽  
Materials Science ◽  
Nanoparticle Synthesis ◽  
Bayesian Optimization ◽  
Absorbance Spectrum ◽  
Nanomaterial Synthesis ◽  
Target Spectrum ◽  
Colour Palette ◽  
Algorithmic Framework

AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

scholarly journals A reconfiguration strategy for modular robots using origami folding

2018 ◽  
Vol 38 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Meibao Yao ◽  
Christoph H. Belke ◽  
Hutao Cui ◽  
Jamie Paik
Keyword(s):  
Heuristic Algorithm ◽  
Minimum Energy ◽  
Computational Time ◽  
Modular Robots ◽  
Automatic Modeling ◽  
Minimum Energy Consumption ◽  
Modular Units ◽  
Optimal Reconfiguration ◽  
Algorithmic Framework ◽  
Modeling Algorithm

Reconfigurability in versatile systems of modular robots is achieved by changing the morphology of the overall structure as well as by connecting and disconnecting modules. Recurrent connectivity changes can cause misalignment that leads to mechanical failure of the system. This paper presents a new approach to reconfiguration, inspired by the art of origami, that eliminates connectivity changes during transformation. Our method consists of an energy-optimal reconfiguration planner that generates an initial 2D assembly pattern and an actuation sequence of the modular units, both resulting in minimum energy consumption. The algorithmic framework includes two approaches, an automatic modeling algorithm as well as a heuristic algorithm. We further demonstrate the effectiveness of our method by applying the algorithms to Mori, a modular origami robot, in simulation. Our results show that the heuristic algorithm yields reconfiguration schemes with high quality, compared with the automatic modeling algorithm, simultaneously saving a considerable amount of computational time and effort.

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