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Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of this paper is to illustrate the dialectical interplay between centralisation and decentralisation forces so as to understand how schools leverage on its autonomous pedagogical space, influence the diffusion of innovations in the educational landscape of Singapore and how a centralised-decentralised system supports (or impedes) pedagogical reform for twenty-first century learning.

The paper first outlines the evolutionary stance of Singapore’s decentralisation from its past to present trajectories, thus providing a broader social-historical interpretation to its tight-loose-tight coupling of the education system; followed by situating the context of reform within the national narrative of Ministry of Education’s (MOE) twenty-first century competencies framework. The authors examine how school autonomy should be accompanied by systemic enabling mechanisms, through two case illustrations of whole-school reforms.

There are four carryover effects that the authors have observed: structural, socio-cultural, economic and epistemic. Middle managers from the two schools act as a pedagogical, socio-technological and financial broker outside the formal collaborative structures organised by the MOE. Such a “middle-out” approach, complemented by centralised mechanisms for “coeval sensing mechanism”, has resulted in boundary-spanning linkages and multiplier effects in terms of knowledge spillovers.

Socio-cultural context matters; and what constitutes as co-learning between policymakers and practitioners in Singapore may be construed as policing that stifles innovations in other contexts.

In addition to the conceptualisation of how school autonomy may lead to school-based innovations, the paper provided some preliminary empirical evidence of how the co-production of knowledge has been engendered within, across and beyond individual Singapore schools through the mechanism of innovation diffusion. The unit of analysis is innovation ecosystem.

This paper combines the concepts of search depth and cognitive proximity to investigate the impact of intense collaboration with different external agents on firms' innovation performance. It empirically tests whether firms that draw deeply on cognitively proximate collaborative partners are more innovative than those collaborating intensively with cognitively distant partners. It explores whether the impact of each external agent is equally important in determining the innovation output of firms.

Using data from the Irish Community Innovation Survey 2012–2014, this paper employs a probit model to empirically test the impact of collaboration with cognitively proximate and distant sources of external knowledge to establish whether their impact on innovation performance is uniform.

The results show that not all collaborators equally impact firm innovation performance. Firms who indicate that knowledge sourced from backward linkages with suppliers is highly important are more likely to engage in both product and process innovation, with the effect more pronounced for the former. The extent of this is greatest for backward linkages compared to forward, horizontal and public linkages. Public linkages have the weakest impact on innovation output which raises questions from a policy perspective given the focus on university–industry collaboration for innovation. The findings indicate that collaboration with cognitively proximate sources of knowledge benefits firms' innovation output.

The study provides empirical evidence on the role of intense collaboration with cognitively proximate and distant external knowledge sources to explore their impact on the subsequent innovation performance of firms. The results can be used to help shape firm-level innovation policy, and indeed national policy, to promote innovation performance.

As Ben Franklin sald, We must all hang together, or assuredly we shall all hang separately.

After considering the material before me, I have formed the opinion that it shall be permitted for the petitioner to examine the file under scrutiny. Deliberation on the case did not take place behind closed doors and there is no lawful prohibition to the examination…in addition I accept the position of the respondent, according to which in spite of the fact that a large portion of the details of the affair were published in the judgment…the file contains material whose revelation can cause unnecessary harm to the central witness…the examination considered will be contingent on an undertaking in writing…according to which the petitioner will not publicize anything that will damage the privacy of the victims and their families beyond the damage that already occurred by the court judgment. (Decision of magistrate Yigaal Marzel, 2006 in the matter of C.A 125/50 Yaakobowitz v. Attorney General)