How to foster innovation in SMEs: evidence of the effectiveness of a project-based technology transfer approach

One of the main challenges in technology transfer is to actively involve small and medium-sized enterprises (SMEs)—which are most in need of and benefit the most from collaborative Research and Development (R&D) programs. This study presents a large-scale collaboration program which focuses on project-based technology transfer in SMEs with little to no prior experience in collaborative research projects. The core of this collaboration program is the temporary secondment of scientists from a Research and Technology Organization (RTO) into an SME to jointly work on a practical project objective—which is directly tailored to the demands of the SME. To evaluate the effectiveness of this approach in overcoming barriers related to finding the right collaboration partner, limited resources, and limited absorptive capabilities, we adopt the R&D Lifecycle Model as a theoretical framework. Our findings, using self-reported and objective data from 106 different projects in a structural equation model, highlight that most SMEs in the considered cluster environment not only successfully mastered a challenging topic in the context of industry 4.0 that immediately benefits the organization, but also engaged in new R&D projects to strengthen their scientific and technical human capital in the long term. Moreover, consistent with previous literature, we found that trust is the main driver within the R&D Lifecycle Model both in building capabilities and economic growth. Based on these insights, we consider a long and close secondment of scientists to SMEs as key for collaboration projects and discuss implications for research and future technology transfer approaches.

(RE)-INTERPRETATION OF THE PAER SPACE CONCEPT AN ENVIRONMENTAL PATTERN APPROACH THROUGH A MULTILEVEL

Lombok is called the Island of Thousand Mosques. Its inhabitants from the Sasak ethnic group are Muslim, who have the concept of the Paer space. It is a place of the existential transience of life, and the center is a mosque. The Paer space is implemented to have a hierarchy from a family cluster environment called paer bale langgak, paer dusun, paer village, and paer cardinal area, which function as the places for worship and culture to spend their remaining time towards an eternal time in the afterlife. This study identifies how religious, social, and cultural activities in the Paer space are centered on the mosque's architecture and interprets the patterns of the community behavior that influence each other in it. The concept of Paer or temporary existential space is then reinterpreted using descriptive-analytical-qualitative methods to obtain a schematic environmental pattern that can be developed adaptively in Muslim communities with mosque as the center of orientation for worship and cultural activities.

ZARP: An automated workflow for processing of RNA-seq data

RNA sequencing (RNA-seq) is a crucial technique for many scientific studies and multiple models, and software packages have been developed for the processing and analysis of such data. Given the plethora of available tools, choosing the most appropriate ones is a time-consuming process that requires an in-depth understanding of the data, as well as of the principles and parameters of each tool. In addition, packages designed for individual tasks are developed in different programming languages and have dependencies of various degrees of complexity, which renders their installation and execution challenging for users with limited computational expertise. The use of workflow languages and execution engines with support for virtualization and encapsulation options such as containers and Conda environments facilitates these tasks considerably. Computational workflows defined in those languages can be reliably shared with the scientific community, enhancing reusability, while improving reproducibility of results by making individual analysis steps more transparent. Here we present ZARP, a general purpose RNA-seq analysis workflow which builds on state-of-the-art software in the field to facilitate the analysis of RNA-seq data sets. ZARP is developed in the Snakemake workflow language using best software development practices. It can run locally or in a cluster environment, generating extensive reports not only of the data but also of the options utilized. It is built using modern technologies with the ultimate goal to reduce the hands-on time for bioinformaticians and non-expert users. ZARP is available under a permissive Open Source license and open to contributions by the scientific community.

Electron ionization of clusters containing the formamide molecule

Studies on electron interactions with formamide (FA) clusters promote scientific interest as a model system to understand phenomena relevant to astrophysical, prebiotic, and radiobiological processes. In this work, mass spectrometric detection of cationic species for both small bare and microhydrated formamide clusters was performed at an electron ionization of 70 eV. Furthermore, a comparative analysis of the cluster spectra with the literature-reported gas-phase spectra is presented and discussed, revealing different reaction channels affected by the cluster environment. This study is essential in developing our understanding of both low-energy electron phenomena in clusters that can bridge the complexity gap between gas and realistic systems and the effect of hydration on electron-induced processes.

Experience of OpenStack Test Deployment and Comparison of Virtual and Real Cluster Environments

Introduction. A brief overview of the properties and architecture of one of the components of the National Cloud of Open Science prototype – the cloud platform OpenStack is given. The list of software and hardware components of the OpenStack test cloud environment and the sequence of actions required for the deployment of both OpenStack itself and the Slurm virtual cluster environment for portable, scalable, reproducible scientific biomedical computing are presented. The purpose of the paper is a description of the experience of test deployment of OpenStack to create a scalable computing environment for reproducible scientific computing using modern technological solutions, which can be applied to both cloud (OpenStack, AWS, Google) and cluster platforms (Slurm). Results. The structure of the created test containerized (using Singularity technology) biomedical application, which contains modern software and libraries and can be used in conventional and cloud virtual cluster environments is briefly described. The results of a comparative test of this application in the virtual cluster environment Slurm under the control of OpenStack and in the node of cluster SKIT-4.5 in the V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine are given. Information on solving the problem of finding the optimal in terms of saving resources scaling parameters for the developed application in two comparable cluster environments is given. Some features of the use of these cluster environments are clarified, in particular, a comparison of the dependence of the application speed on the number of parallel processes for two cluster environments is presented. Empirical data are presented in graphical form, which illustrate the nature of the load on the OpenStack server and the use of RAM on the number of parallel processes. Possibilities of portability between the specified cluster environments, scaling of calculations and maintenance of reproducibility of calculations for the offered test application are demonstrated. The advantages of using OpenStack technology for scientific biomedical calculations are pointed out. Conclusions. The described example of test deployment and use of OpenStack gives an idea of the requirements for the necessary technical base to ensure the reproducibility of scientific biomedical calculations in cloud and cluster environments. Keywords: cloud technologies, reproducible calculations, cluster platform.

The dwarf galaxy population in nearby clusters from the KIWICS survey

ABSTRACT We analyse a sample of 12 galaxy clusters, from the Kapteyn IAC WEAVE INT Cluster Survey (KIWICS) looking for dwarf galaxy candidates. By using photometric data in the r and g bands from the Wide Field Camera (WFC) at the 2.5-m Isaac Newton Telescope (INT), we select a sample of bright dwarf galaxies (M$_r\, \le$ −15.5 mag) in each cluster and analyse their spatial distribution, stellar colour, and as well as their Sérsic index and effective radius. We quantify the dwarf fraction inside the R200 radius of each cluster, which ranges from ∼0.7 to ∼0.9. Additionally, when comparing the fraction in the inner region with the outermost region of the clusters, we find that the fraction of dwarfs tends to increase going to the outer regions. We also study the clustercentric distance distribution of dwarf and giant galaxies (M$_r\, \lt $ −19.0 mag), and in half of the clusters of our sample, the dwarfs are distributed in a statistically different way as the giants, with the giant galaxies being closer to the cluster centre. We analyse the stellar colour of the dwarf candidates and quantify the fraction of blue dwarfs inside the R200 radius, which is found to be less than ∼0.4, but increases with distance from the cluster centre. Regarding the structural parameters, the Sérsic index for the dwarfs we visually classify as early-type dwarfs tends to be higher in the inner region of the cluster. These results indicate the role that the cluster environment plays in shaping the observational properties of low-mass haloes.

An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) leverages machine learning models to help practitioners handle the massive data produced during the operations of large-scale systems. However, due to the nature of the operation data, AIOps modeling faces several data splitting-related challenges, such as imbalanced data, data leakage, and concept drift. In this work, we study the data leakage and concept drift challenges in the context of AIOps and evaluate the impact of different modeling decisions on such challenges. Specifically, we perform a case study on two commonly studied AIOps applications: (1) predicting job failures based on trace data from a large-scale cluster environment and (2) predicting disk failures based on disk monitoring data from a large-scale cloud storage environment. First, we observe that the data leakage issue exists in AIOps solutions. Using a time-based splitting of training and validation datasets can significantly reduce such data leakage, making it more appropriate than using a random splitting in the AIOps context. Second, we show that AIOps solutions suffer from concept drift. Periodically updating AIOps models can help mitigate the impact of such concept drift, while the performance benefit and the modeling cost of increasing the update frequency depend largely on the application data and the used models. Our findings encourage future studies and practices on developing AIOps solutions to pay attention to their data-splitting decisions to handle the data leakage and concept drift challenges.