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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。

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.

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.

This chapter examines how Industrial Revolution 4.0 (IR 4.0) influences shape and reshapes the tourism industry. Tourism 4.0 technology enables user engagement with a system, improvement of the real tourist experience and the development of new approaches to influence behaviour change and even long-term user transformation.

Using an in-depth review of existing data, this chapter explains IR 4.0 and its integration into the tourism industry, especially on tourist behaviour.

Although technology integrates very slowly into the tourism industry, its phase of integration, especially Industry 4.0, is highly evident. Technology proved that it could enhance products and services in tourism and with its optimal use it can sustainably integrate into the tourism industry.

Service is still the keyword for tourism, and at a certain level, services are integrated into the tourism industry for standardisation and consistency. The recent global pandemic paves the way for several alternatives to integrate technology into the services.

This study aims to develop an experimentally validated three-dimensional numerical model for predicting different flow patterns produced with a gas dynamic virtual nozzle (GDVN).

The physical model is posed in the mixture formulation and copes with the unsteady, incompressible, isothermal, Newtonian, low turbulent two-phase flow. The computational fluid dynamics numerical solution is based on the half-space finite volume discretisation. The geo-reconstruct volume-of-fluid scheme tracks the interphase boundary between the gas and the liquid. To ensure numerical stability in the transition regime and adequately account for turbulent behaviour, the k-ω shear stress transport turbulence model is used. The model is validated by comparison with the experimental measurements on a vertical, downward-positioned GDVN configuration. Three different combinations of air and water volumetric flow rates have been solved numerically in the range of Reynolds numbers for airflow 1,009–2,596 and water 61–133, respectively, at Weber numbers 1.2–6.2.

The half-space symmetry allows the numerical reconstruction of the dripping, jetting and indication of the whipping mode. The kinetic energy transfer from the gas to the liquid is analysed, and locations with locally increased gas kinetic energy are observed. The calculated jet shapes reasonably well match the experimentally obtained high-speed camera videos.

The model is used for the virtual studies of new GDVN nozzle designs and optimisation of their operation.

To the best of the authors’ knowledge, the developed model numerically reconstructs all three GDVN flow regimes for the first time.

The aim of this paper states that total productive maintenance (TPM) is an improvement tool which employs the effective utilization of employees in order to enhance the reliability of the equipment in consideration.

This paper identifies and evaluates the factors accountable for the adoption of TPM methodology in manufacturing organizations. Twenty-four factors affecting the TPM implementation are explored and categorized into five significant categories. Afterwards, these identified TPM factors have been evaluated by using a most popular Multi-criteria decision-making (MCDM) approach namely fuzzy pivot pairwise relative criteria importance assessment (F-PIPRECIA).

In this paper, through application of F-PIPRECIA, “Behavioural factor” is ranked first while “Financial factor” the last. Considering the sub-factors, “Top management support and commitment” is ranked first while “Effective use of performance indices” is ranked the last. A further sensitivity analysis indicates the factors that need higher level of attention.

The result of current research work may be exploited by the top administration of manufacturing enterprises for assessing their TPM adoption status and to recognize the frail links of TPM application and improve accordingly. Moreover, significant factors of TPM can be identified and deploy them successfully in their implementation procedure.

The conclusion obtained from this research enables the management to clearly understand the significance of each considered factor on the adoption of TPM in the organization and hence, provides effective utilization of resources.

The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs.

Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement.

The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load.

The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.