Closing the Performance Gap with Modern C++

2016 ◽  
pp. 18-31 ◽  
Author(s):  
Thomas Heller ◽  
Hartmut Kaiser ◽  
Patrick Diehl ◽  
Dietmar Fey ◽  
Marc Alexander Schweitzer
Keyword(s):  
Performance Gap

Investors’ Irrationality - The Determinants of the Performance Gap for Open-End Real Estate Funds

2018 ◽  
Author(s):  
Carsten Fritz ◽  
Steffen Sebastian ◽  
René-Ojas Woltering
Keyword(s):  
Real Estate ◽  
Performance Gap

Keeping Up With the Joneses: Role of CSR Awards in Incentivizing Non-Winners’ CSR

2020 ◽  
pp. 000765032098227
Author(s):  
Jiangyan Li ◽  
Juelin Yin ◽  
Wei Shi ◽  
Xiwei Yi
Keyword(s):  
Corporate Social Responsibility ◽  
Spillover Effect ◽  
Competitive Dynamics ◽  
Difference In Differences ◽  
Performance Gap ◽  
The Media ◽  
Catch Up ◽  
Corporate Social ◽  
Meaningful Event

We attempt to provide a novel antecedent of corporate social responsibility (CSR) by focusing on the role of CSR awards. Specifically, we investigate how competitors’ winning CSR awards incentivize non-winning firms’ CSR as a competitive catch-up. Using a difference-in-differences research design, we find that non-winners improve their CSR after their competitors have won CSR awards. Furthermore, based on the awareness-motivation-capability (AMC) framework from the competitive dynamics literature, we find that the media visibility of award winners, the performance gap of non-winners with award winners, and the prior CSR of non-winners strengthen the CSR competitive catch-up behaviors. Findings from this study contribute to the CSR research by highlighting the spillover effect of CSR awards as a meaningful event in incentivizing non-winning firms’ CSR and extending the AMC framework to explain the contingency factors of competitive catch-up in the context of CSR research.

scholarly journals Managing energy performance in buildings from design to operation using modelling and calibration

2021 ◽  
pp. 014362442110083
Author(s):  
Nishesh Jain ◽  
Esfand Burman ◽  
Dejan Mumovic ◽  
Mike Davies
Keyword(s):  
Best Practice ◽  
Good Practice ◽  
Energy Performance ◽  
Operating Conditions ◽  
Design Stage ◽  
Performance Gap ◽  
Actual Performance ◽  
Simulation Tools ◽  
Calibration Protocol ◽  
Covering Design

To manage the concerns regarding the energy performance gap in buildings, a structured and longitudinal performance assessment of buildings, covering design through to operation, is necessary. Modelling can form an integral part of this process by ensuring that a good practice design stage modelling is followed by an ongoing evaluation of operational stage performance using a robust calibration protocol. In this paper, we demonstrate, via a case study of an office building, how a good practice design stage model can be fine-tuned for operational stage using a new framework that helps validate the causes for deviations of actual performance from design intents. This paper maps the modelling based process of tracking building performance from design to operation, identifying the various types of performance gaps. Further, during the operational stage, the framework provides a systematic way to separate the effect of (i) operating conditions that are driven by the building’s actual function and occupancy as compared with the design assumptions, and (ii) the effect of potential technical issues that cause underperformance. As the identification of issues is based on energy modelling, the process requires use of advanced and well-documented simulation tools. The paper concludes with providing an outline of the software platform requirements needed to generate robust design models and their calibration for operational performance assessments. Practical application The paper’s findings are a useful guide for building industry professionals to manage the performance gap with appropriate accuracy through a robust methodology in an easy to use workflow. The methodological framework to analyse building energy performance in-use links best practice design stage modelling guidance with a robust operational stage investigation. It helps designers, contractors, building managers and other stakeholders with an understanding of procedures to follow to undertake an effective measurement and verification exercise.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

Bridging the Performance Gap: The Body, Cognitive Theory, and Comedia Studies

2003 ◽  
Vol 55 (2) ◽  
pp. 11-53
Author(s):  
Catherine Connor (Swietlicki)
Keyword(s):  
Cognitive Theory ◽  
The Body ◽  
Performance Gap

Developing High Performance GaP/Si Heterojunction Solar Cells

10.3791/58292 ◽  
2018 ◽  
Author(s):  
Chaomin Zhang ◽  
Ehsan Vadiee ◽  
Som Dahal ◽  
Richard R. King ◽  
Christiana B. Honsberg
Keyword(s):  
Solar Cells ◽  
High Performance ◽  
Performance Gap ◽  
Heterojunction Solar Cells
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