I-WASP—Intelligent and Wireless Technologies for Automatically Generated Shift Protocols in Conventional Tunnelling

Due to the liberalization and transformation of emerging markets economies, the attractiveness of these countries for foreign direct investors has been rising in the last decades. This paper explores foreign direct investment location choice factors of German and Austrian companies in Brazilian regions. We perform the quantitative analysis, based on the Multinomial Nested Logit Model and supplement its findings by the qualitative analysis, based on the semi-structured experts’ interview. The analyses shown that investor-nation specific agglomeration, industry specialization, workforce qualification and physical infrastructure were important FDI location choice factors for German and Austrian companies in Brazil. Suggestions for future research of the FDI location choice factors are discussed.

<p>Concrete is a multifunctional construction material and currently the most employed one. Buildings built with reinforced concrete can provide excellent structures. However, current reinforced concrete constructions have a drawback – steel in concrete can corrode and can cause significant damage. To prevent this, concrete, dependent on its ambient surrounding, has to be protected by a thick concrete cover. This leads to a high material, resource and energy consumption and notable carbon dioxide emissions. Furthermore, the service life of reinforced concrete is still limited to 40 to 80 years.</p><p>For decades, an alternative to steel reinforcement has been searched for. In Germany, the focus has been laid on non-metallic reinforcements such as glass and carbon. In the 1990s, basic research has begun in the field of textile reinforcement and shown very promising results. For 10 years now the material has been slowly transferred into practice.</p><p>For German companies and research institutions as well as the German government there is a future in carbon reinforced concrete – a further development of textile reinforced concrete (TRC). Carbon reinforced concrete shall be used for new constructions and for the strengthening of existing structures. Germany wants to build more sustainable and energy-efficient in the future and wants to reduce quickly the existing investment backlog. The market launch shallbe quick and extensive.</p><p>However, there are still numerous conditions that need to be established, e.g. thicker cross sections, suitable connection devices, economic production processes and appropriate guidelines. To set up these conditions, Germany is currently investing about 65 million euros(45 million euros public funds plus around20 million euros company funds) in the quick exploration of the material and anew construction design.More than 130 companies and research institutions from different industrial sectors participate in the currently largest research project C3 – Carbon Concrete Composite in the building industry. The aim is to establish all necessary conditions in order to widely apply the new construction design by 2020.</p><p>This paper provides an insight into the topic carbon reinforced concrete, the overall research project and all ongoing particular projects</p>

Sensor networks are commonly used in a wide area of possible applications. In such network there is a great demand for data transmission. Data can be transmitted either by cable or wireless. This paper presents possible solution for data transmission - a wireless platform which consists of devices providing data acquisition, transmission and processing. The paper includes also construction and potential functions of the network node.

Teleteaching scenarios for high bandwidth networks

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

To meet the rising demand for comprehensive bioinformatics tools in the life sciences, the German Network for Bioinformatics Infrastructure (de.NBI) was established in 2015 as a national bioinformatics consortium offering high quality bioinformatics services, comprehensive training and powerful computing capacities. The de.NBI Cloud is one of the flagship services for the de.NBI & ELIXIR Germany network with more than 4000 registered users, over 1350 projects, of which more than 620 are currently active, and over 1380 publications citing its use. Eight federated cloud centers adhere to a unified governance structure and utilize the centralized project application and management workflow available through the de.NBI Cloud Portal. Single sign-on and user management are provided via LifeScience Login and eduGAIN. PIs from German universities or research institutes may submit project applications, which are reviewed by the Cloud Access Committee and, upon approval, resources are allocated to one of the cloud centers. Regular staff training, mutual internal audits and external ISMS certification (ISO27001 or BSI C5) ensure secure operations of the data centers. The de.NBI Cloud offers various project types for your computing needs, covering different levels of expertise and experience in cloud computing: From the simple and fast start of pre-configured VMs with easy access (SimpleVM), to the use of bioinformatics tools and workflows (Galaxy), to the possibility of running your own infrastructures and services (OpenStack & Kubernetes). We offer pre-configured virtual research environments (VRE), which can be accessed easily when using our SimpleVM platform. They include integrated development environments, data science notebooks and virtual desktop environments. The VREs can be accessed in the web browser or by secure shell (ssh). VREs and images tailored for specific needs can also be used to provide uniform VMs for trainings and workshops. The cluster module makes it easy to set up a SLURM-based grid cluster for resource-intensive computations. Users can also provide recipes for custom VREs. The de.NBI Cloud provides computational resources for a diverse portfolio of projects within Europe (ELIXIR, EOSC, Horizon Europe) and Germany (NFDI projects), ranging from (web)service development and hosting, to execution of memory and CPU-intensive bioinformatics workflows, GPU resources for machine learning, resources for hackathons, workshops and trainings, as well as resources for proof-of-concept development, and secure environments for sensitive data processing. Regular trainings and workshops teach you how to use the cloud infrastructure effectively for your research. The annual Cloud User Meeting offers users the opportunity for networking and to get in touch with the administrators of the cloud locations and the cloud governance. Researchers present their work in the cloud and training courses expand the range of expertise for experienced cloud users, while also providing a comprehensive introduction for beginners. The de.NBI Cloud offers various project types for your computing needs, covering different levels of expertise and experience in cloud computing: From the simple and fast start of pre-configured VMs with easy access (SimpleVM), to the use of bioinformatics tools and workflows (Galaxy), to the possibility of running your own infrastructures and services (OpenStack & Kubernetes). Selection of training course topics: • VM basics: Linux & command line • SimpleVM & Research Environments • BioConda & BioContainers • CWL, Nextflow, Snakemake • Infrastructure as Code: Ansible, Terraform • Grid computing: BiBiGrid / SLURM

This paper presents a comprehensive overview of the state of the art and development trends of a vehicle electric system also known as (vehicle) electrical 12 V system, (vehicle) on board network and electric and electronic (e/e) architecture. This summary is based on the results of an extensive literature research covering the recent ten years as well as a set of expert statements collected by the author with the help of interviews with German OES, OEM, and research institutes.

Mastering challenging times and looking with optimism into the future

Wireless data communications commonly use public networks to exchange data packets. Public networks of rural area are lack of network infrastructure so it will inflict high delay for wireless communications. The rural areas are same with intermittent area that need a mechanism of data transfer to tolerate with delay. The Delay Tolerant Network (DTN) is a solution for data transmission in rural areas that have coverless TCP/IP network. DTN uses store-and-forward mechanism to send data packets from the source to the destination through intermediary nodes. DTN uses dynamic routing protocols to determine routes of data packets delivery. DTN implements a reliable routing protocol of Optimized Link State Routing (OLSR) algorithm to looking for a delivery route of data packets to the end node. The OLSR routing protocol uses MPRs node as selected nodes, which forwards the control messages of networks topology information. This could streamline the network traffic of DTN to determine the best route of delivery data packets from the sender to the receiver. This paper presents transmission of medical data from the source of the node in the rural area to the destination node through the nodes of intermediate. This telemedicine system sends medical data of patient's private data that is encrypted using Advanced Encryption System (AES) to guarantee the medical data safety. The AES encryption system takes no more than 0.4 ms for data encrypting and decrypting process.

Interest in Unmanned Aerial Vehicles (UAVs) has been increasingly growing in recent years, especially for purposes other than those for which they were initially used (civil and military purposes). Currently, in fact, they are used for advanced monitoring and control purposes, for 3D reconstructions of the territory and cultural heritage, and for freight transport. The problem in using these systems consists of the limited flight autonomy. In fact, commercially used drones, today, are sold with a set of batteries of limited duration which do not allow flights over large areas and, therefore, detailed surveys. The present work seeks to overcome these limitations by proposing an intelligent automatic charging system (Intelligent Charging Network) created using PC Engines Alix and an experimental drone prototype using a Raspberry Pi 3 and a Navio 2 module. At the same time, an efficient Intelligent Charging Network–drone communication system and a data transmission system are proposed, which allow images acquired by the drone to be transferred directly to the server used for data storage for their subsequent processing as well as the transmission of the flight plan from the QGroundControl application to the drone. The proposed system represents technological innovation in the field of drones with potential future developments linked to the implementation of sustainable drones.

Recent progress of computer networks realizes advanced network services such as high-resolution video streaming and Internet of things (IoT) but it requires further huge amount of traffic. To expand transmission capacity, various studies develop time division multiplexing (TDM), wavelength division multiplexing (WDM), and spatial division multiplexing (SDM) technologies. Time-division packet spatial super-channel (pSSC) is expected as one of promising solutions to realize high throughput with low cost. We have developed a switching node system for the pSSC transmissions. The pSSC is suitable for large data transmission. On the other hand, overhead of pSSC is not negligible in the case of small size data transmissions. This paper proposes a fractional pSSC switching system design for both large and small data transmissions. We introduce the spatial super-channel slicing concept and ILP solution for effective data transmission having bimodal distribution of data size. Effect on slice spatial channel was validated through simulation where it is 50% better than no slicing system.

Processing in Memory based on memristors is considered the most effective solution to overcome the Von Neumann bottleneck issue and has become a hot research topic. The execution efficiency of logical computation and in-memory data transmission is crucial for Processing in Memory. This paper presents a design scheme for data transmission and multi-bit multipliers within MAT (a data storage set in MPU) based on the memristive alternating crossbar array structure. Firstly, to improve the data transfer efficiency, we reserve the edge row and column of the array as assistant cells for OR AND (OA) and AND data transmission logic operations to reduce the data transfer steps. Furthermore, we convert the multipliers into multi-bit addition operations via Multiple Input Multiple Output (MIMO) logical operations, which effectively improves the execution efficiency of multipliers. PSpice simulation shows that the proposed data transmission and multi-bit multiplier solution has lower latency and power consumption and higher efficiency and flexibility.

Should research institutions from developing countries provide open access to their data? Who would benefit? These are highly relevant questions for environmental research. In the backdrop of increasing discussions regarding open data, attention should be paid to guarantee that research institutions from developing countries do not become mere data providers. At the same time, restricted access to data should not become a barrier for advancing our knowledge of critical environmental issues. Open access policies for environmental data must be developed not only for advancing science, but also to provide growing opportunities for researchers in developing countries.

In this talk we will present the SUPRENUM multiprocessor system and some implementations of parallelized sparse matrix algorithms. The SUPRENUM multiprocessor system was delivered late in 1989 for the first time. It is the result of a research project where German research institutes, universities and industrial companies worked together to built a 256 processor distributed memory machine. In parallel with the construction of the SUPRENUM a lot of time and man power was invested for the software support of the project. As an important application in scientific computation we parallelized the solution of systems of linear equations Ax=b. For realistic problems the large coefficient matrix A is sparse most of the time, i.e. a large number of its entries are zero. We show how direct algorithms based on Gauss Elimination and semi-iterative algorithms (Conjugate Gradient Methods) can be implemented on SUPRENUM. Especially the Conjugate Gradient Methods which are very well suited for parallelization and vectorization proved to be very efficient on multiprocessor architectures.

The article is devoted to the topical issues of artificial intelligence (AI) technology for managing the optimization of business processes under the influence of changing SEO trends in 2025, which is important and requires the identification of the main vectors of their further development for implementation in the work of the business. In the modern world, where information has a dynamic development and becomes a digital asset, the use of AI plays a significant role for managers and clients, which determines the development of the functional intelligence of the future management network. The research consists of analyzing the main approaches in the context of business process management regarding their optimization in the field of security, taking into account the changing trends of SEO in 2025 using AI, which will contribute to increasing the efficiency of business decisions. The main trends of AI influence on the development of SEO optimization in website promotion in 2025, which has a positive impact on business development and the effectiveness of indicators, are summarized. The vectors of management efficiency through the impact of AI technology on the optimization of business processes in the field of security services, which will contribute to timely response to the challenges and risks of the modern competitive environment, are determined. In conclusion, it is emphasized that for improving the management of business processes regarding their optimization, it is necessary to use AI, in particular in the field of security, taking into account SEO trends in 2025, through the analysis of current processes, identifying priority goals of business process optimization, improving existing processes and developing new ones taking into account the use of modern technologies and monitoring business results by implementing changes in order to adjust them. These vectors contribute to the optimization of business processes in the field of security, but there is a need to take into account the cost of AI implementation and training of security guards who have to work with AI technology and understand how to interpret the data obtained. An important challenge is the solution of an ethical issue that concerns the confidentiality and protection of personal data of clients. It has been found that AI has significant potential for optimizing business processes in the field of security regarding increasing management efficiency, improving the quality of service, which will lead to business performance. The vector of further research lies in the wide application of AI in management, which will bring the business to a qualitatively new level of management, defining the development of the functional intelligence of the future management network.