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This paper aims to explore the key anchors of the National Innovation System shaping the nature of collaboration between academic high-performance computing centres (academic HPC centres) and small- to medium-sized enterprises (SMEs) working in the automotive and electronics sectors of the Danube region. With two main research questions, it discusses the importance of knowledge transfer and technology transfer for collaboration between University and Industry (U-I collaboration) in three groups of developmentally distinct countries: competitively advanced, competitively intermediate and competitively lagging. As main anchors of the innovation system, stable legal environment, exciting innovation policies and strong R&D funding are recognised.

A qualitative empirical study in 14 Danube region countries included 92 focus group participants, expert representatives of academic HPC centres and SMEs. The data were audio recorded, transcribed and analysed.

The findings show the main prerequisites of the framework conditions for efficient U-I collaboration evolve through a goal-oriented National Innovation Policy and developed and functioning legal environment supporting labour market and intellectual property (IP) protection and enforcement. Additionally, skilled people are needed to be able to operate with HPC, where it seems all the countries lack such skilled workforce. In competitively lagging countries, the high levels of brain drain exhibit strong impact to U-I collaboration.

Research into relationships between academic HPC centres and SMEs conducted was qualitative; therefore, limitations in terms of generalisation arise from it. On the other hand, the research is promising in terms of offering the guidance for policy makers who can use the findings when delivering innovation policy mix, adjusted to developmental level of own innovation ecosystem.

The study is among the pioneering work in U-I collaboration between academic HPC centres and SMEs from automotive and electronics industries in the Danube region. The research addresses the dynamics of collaboration and offers policy implications to strengthen the particular U-I collaboration.

本文旨在探究國家創新系統的主要支柱; 這些支柱決定了學術性的高速網路與計算中心 (註: 此為直譯) (以下簡稱學術高網算中心) 與於多瑙河地區的汽車製造業和電子產品行業內營運的中小型企業之間的合作性質。本文透過兩條主要的研究問題、去探討知識轉移和技術轉讓對大學與產業界之間的合作的重要性而這些產業是屬於在發展階段上三個明顯不同的國家組別裏的這三個組別是 競爭先進的、競爭性中級的和競爭落後的。穩定的法律環境、令人興奮的創新政策和強大的研究與開發資金被認為是創新系統的三個主要支柱。

研究人員在14個位於多瑙河地區的國家裏進行一個質性觀察研究研究涵蓋92個焦點小組參與者、來自學術高網算中心和中小型企業的專家代表。有關的數據被錄音繼而被轉寫下來最後被分析。

研究結果顯示效率高的大學產業界合作的框架條件的主要先決條件是透過一個以目標為導向的國家創新政策而逐漸形成繼而發展起來; 另外所需的條件是一個支援勞工市場、保障知識產權、並執行有關的法律的正常運作的法律環境。其次若想與學術高網算中心一起工作技術人才是必須的因學術高網算中心內的所有國家似乎欠缺技術勞動力。在落後於競爭對手的國家裏高度的人才外流對大學與產業界之間的合作會產生重大的影響。

由於研究採用的研究方法為質性研究法故研究結果、就普遍化的歸納而言是有其局限的。唯研究結果在實務方面有其作用因政策制定者在推行與科技進步與對策有關的策略時他們可把研究結果作為指引就其自身創新生態系統的發展水準而作出適當的調整。

本研究探討涉及學術高網算中心與於多瑙河地區的汽車製造業和電子產品行業內營運的中小型企業之間合作的大學產業界合作就此而言可說是開創性研究之一。本研究探究有關的大學產業界合作的變革動力並為政策制定者提供啟示以能強化有關的合作。

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

In the past decade, in the space industry, many initiatives intended at offering open access to big data from space multiplied. Therefore, firms started adopting business models (BMs) which lever on digital technologies (e.g. cloud computing, high-performance computing and artificial intelligence), to seize these opportunities. Within this scenario, this article aims at answering the following research question: which digital technologies do impact which components the BM is made of?

An exploratory multiple case study approach was used. Three cases operating in the space industry that lever on digital technologies to implement their business were analyzed. Despite concerns regarding reliability and validity, multiple case studies allow greater understanding of causality, and show superiority respect to quantitative studies for theory building.

Big data, system integration (artificial intelligence, high-performance computing) and cloud computing seem to be pivotal in the space industry. It emerges that digital technologies involve all the different areas and components of the BM.

This paper sheds light on the impact that digital technologies have on the different BM components. It is only understanding which technologies can support the value proposition, which technologies make the infrastructural part able to support this proposition, which technologies may be helpful for delivering and communicating this value to customers and which technologies may help firms to appropriate the value that it is possible to seize the impact of digital technologies on BM.

The purpose of this paper is to resolve three problems in ship routing and scheduling systems. Problem 1 is the anticipation of the future cargo transport demand when the shipping models are stochastic based on this demand. Problem 2 is the capacity of these models in processing large number of ships and cargoes within a reasonable time. Problem 3 is the viability of tramp shipping when it comes to real problems.

A commodity-trade forecasting system is developed, an information technology platform is designed and new shipping elements are added to the models to resolve tramp problems of en-route ship bunkering, low-tide port calls and hold-cleaning cost caused by carrying incompatible cargoes.

More realistic stochastic cargo quantity and freight can now be anticipated, larger number of ships and cargoes are now processed in time and shipping systems are becoming more viable.

More support goes to ship owners to make better shipping decisions.

New norms are established in forecasting, upscaling and viability in ship routing and scheduling systems.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.