Key factors of adopting energy management systems in building sector in Taiwan

It is no doubt the world is experiencing global warming and human emission causes distinct climate change. According to the analysis from International Energy Agency, the over consumed nature resources from power, industry, transport and building sectors are off the major factors reflecting on the essential greenhouse gases to life. As moving onto the smart technology and smart grid development, recent studies indicate that using the advanced energy management systems is critical to improve power efficiency, energy saving, and reduce greenhouse gas emission. This study relies on a systematic literature review and expert opinion to identify the critical factors of adoption building energy management system. Finally, a framework is presented to evaluate the introduction of energy management systems in the construction field in order to achieve zero carbon ready buildings

Correction: Chang, C.W., et al. Development of a Three Dimensional Neural Sensing Device by a Stacking Method. Sensors 2010, 10, 4238–4252

The authors would like to correct the affiliations and acknowledgement of this paper [1] as follows: [...

THE EFFECT OF INNOVATION STRATEGY ON POST-M&A INNOVATION PERFORMANCE: AN EVIDENCE FROM PHARMACEUTICAL INDUSTRY

M&A is a popular strategy for pharmaceutical industry due to high R&D risk and costs. Prior research related to post-M&A performance mainly focused on the financial and technology resource perspectives. This study aims to provide a new perspective of innovation strategy which is inspired by the research of March (1991), who noted the difference between exploration and exploitation. Moreover, we build the bridge between M&A and innovation strategy by applying the resource-based view theory. We argue that the acquirer’s exploration strategy will negatively influence the post-M&A innovation performance and the innovation strategy similarity between the acquirer and the target is beneficial for future innovation. Furthermore, we hypothesize that there is a negatively moderating effect caused by the acquirer’s exploration strategy on the effect of innovation strategy similarity. On the basis of 89 M&A deals in the pharmaceutical industry, our empirical results suggest two important findings. First, post- M&A innovation performance is influenced by acquirer’s innovation strategy, more specifically, acquirer’s exploration is harmful for post-M&A innovation. Second, the similarity effect is moderated by acquirer’s innovation strategy. Precisely, acquirer’s exploration will diminish the positive effect of similarity

Reynolds-Averaged Turbulence Modeling Using Type I and Type II Machine Learning Frameworks with Deep Learning

Deep learning (DL)-based Reynolds stress with its capability to leverage values of large data can be used to close Reynolds-averaged Navier-Stoke (RANS) equations. Type I and Type II machine learning (ML) frameworks are studied to investigate data and flow feature requirements while training DL-based Reynolds stress. The paper presents a method, flow features coverage mapping (FFCM), to quantify the physics coverage of DL-based closures that can be used to examine the sufficiency of training data points as well as input flow features for data-driven turbulence models. Three case studies are formulated to demonstrate the properties of Type I and Type II ML. The first case indicates that errors of RANS equations with DL-based Reynolds stress by Type I ML are accumulated along with the simulation time when training data do not sufficiently cover transient details. The second case uses Type I ML to show that DL can figure out time history of flow transients from data sampled at various times. The case study also shows that the necessary and sufficient flow features of DL-based closures are first-order spatial derivatives of velocity fields. The last case demonstrates the limitation of Type II ML for unsteady flow simulation. Type II ML requires initial conditions to be sufficiently close to reference data. Then reference data can be used to improve RANS simulation