Advanced Flat Cable Innovations for Scalable and Flexible System Architectures

The described proof-of-concept demonstrates, how manifest manipulation enables dynamic ad insertion and flexible over-the-top streaming workflows including ondemand-to-live services across a multitude of devices and platforms via MPEG-DASH, HLS and hybrid TV environments like HbbTV. Content manipulation and dynamic ad insertion is realized through non-video-intrusive technologies operating on manifest level in a highly flexible and scalable system architecture. APIs allow for individual stream management and channel orchestration. Dynamic digital video contents and advertising can be integrated per user and on the fly, in both linear and ondemand content. The evaluated system incorporates recent content stitching and content insertion techniques as DASH events, Xlink, SCTE35 and manifest rewriting for on demand and live sources. The different technologies have been evaluated against state-of-the art HLS and MPEG-DASH video players in multiscreen environments to derive best practices for short term utilization. In addition, recommendations for content creation and player implementations have been evaluated to further support the integration of those technologies in future solutions.

The ubiquity of cloud computing is shifting the deployment of Business Process Management Systems (BPMS) from traditional on-premise models to the Software-as-a-Service (SaaS) paradigm, thus aiming to deliver Business Process Automation as a Service to multiple tenants in the cloud. However, scaling up a traditional BPMS to cope with simultaneous demand from multiple organisations in the cloud is challenging, since its underlying system architecture has been designed to serve a single organisation with a single workflow engine. A typical SaaS often deploys multiple instances of its core applications and distributes workload to these application instances via load balancing. But, for stateful and often long-running process instances, standard stateless load balancing strategies are inadequate. In this paper, we propose a conceptual design of a scalable system architecture for deploying BPMS in the cloud. In our design, Object Role Modeling (ORM) is used to conceptualise the data requirements of the system and UML sequence diagrams are used to capture the interactions between system components. A prototypical implementation using an open-source traditional BPMS offers focused load balancing strategies and demonstrates improved capabilities for supporting large volumes of work in a multi-tenanted cloud environment.

Single processor performance has exhibited substantial growth over the last three decades [1] as shown in Figure 1. What is also desired are techniques which enable connecting together multiple processors in order to create scalable, modular and resilient multiprocessor systems. Beginning with the production of the Intel® Xeon® processor 5500 series, (previously codenamed “Nehalem-EP”), the Intel® Xeon® processor 7500 series (previously codenamed “Nehalem-EX”), and the Intel® Itanium™ processor 9300 series (previously codenamed “Tukwila-MC”), Intel Corporation has introduced a series of multi-core processors that can be easily interconnected to create server systems scaling from 2 to 8 sockets. In addition, OEM platforms are currently available that extend this up to 256-socket server designs1. This scalable system architecture is built upon the foundation of the Intel® QuickPath Interconnect (Intel QPI). These Intel micro-architectures provide multiple high-speed (currently up to 25.6 GB/s), point-to-point connections between processors, I/O hubs and third party node controllers. The interconnect features, as well as the capabilities built into the processor’s system interconnect logic (also known as “uncore”), work together to deliver the performance, scalability, and reliability demanded in larger scale systems.

Energy conversion and distribution of heat and electricity is characterized by long planning horizons, investment periods and depreciation times, and it is thus difficult to plan and tell the technology that optimally fits for decades. Uncertainties include future energy prices, applicable subsidies, regulation, and even the evolution of market designs. To achieve higher adaptability to arbitrary transition paths, a technical concept based on integrated energy systems is envisioned and described. The problem of intermediate steps of evolution is tackled by introducing a novel paradigm in urban infrastructure design. It builds on standardization, modularization and economies of scale for underlying conversion units. Building on conceptual arguments for such a platform, it is then argued how actors like (among others) municipalities and district heating system operators can use this as a practical starting point for a manageable and smooth transition towards more environmental friendly supply technologies, and to commit to their own pace of transition (bearable investment/risk). Merits are not only supported by technical arguments but also by strategical and societal prospects like technology neutrality and availability of real options.

Augmented reality technology is useful for interaction with robotic devices. A user can see the status of and direct orders to robots in the actual operating environment rather than on a remote computer console. A system is presented where fiducial markers are used for the dual purpose of reading the position of a multi-robot system and for user interaction using augmented reality. A user can observe, with the assistance of a hand-held device or wearable eyepiece display, the planned motion and tasks of the robots. An intuitive interface allows the robots to be retasked dynamically. A scalable system architecture and a prototype is presented.

Telementoring in surgical training enables expert surgeons to provide real-time remote guidance to trainees. This technique is increasingly adopted to address surgical training gaps in low- and middle-income countries (LMICs), i.e., nations with a gross national income per capita of $13,845 or less. However, existing systems are constrained by conventional communication systems, which experience high latency, limited bandwidth, and poor video resolution. These challenges hinder the integration of immersive and interactive technologies such as virtual reality (VR), augmented reality (AR), and haptic feedback, which would enhance learning outcomes, especially in resource-constrained environments. AR overlays digital images onto the real-world environment, whereas VR immerses users in a fully computer-generated environment, enabling trainees to interact with surgical components as if they are in the real world. In addition, the advent of the fifth generation of mobile networks (5G), which delivers ultra-low latency, high bandwidth, and support for network slicing, offers a promising foundation for scaling high-fidelity telementoring systems. Moreover, the integration of artificial intelligence (AI), i.e., equipping computer systems with the ability to perform tasks that would typically require human intelligence, can enable real-time performance analytics and skill assessment of the trainees. In regions lacking reliable network backbone infrastructure, such as fiber, hybrid approaches that combine low-cost 5G deployments with satellite communication can be leveraged to achieve reliable end-to-end connectivity. Therefore, this systematic literature review evaluates current surgical telementoring systems, their enabling technologies, and associated challenges, with emphasis on LMIC contexts, where such systems will have the greatest benefits. Through a structured Population, Intervention, Comparison, and Outcome (PICO)-based synthesis, we address five key research questions that span key aspects such as current telementoring systems and technologies, functional and technical requirements, educational outcome effectiveness, and the gaps addressed by modern technology and connection solutions, i.e., 5G, AI, VR, and AR. In addition, we identify recurrent challenges and propose a design framework that can be adopted in low-resource settings. Finally, we outline future development directions, including AI-driven evaluation models, scalable system architectures, and policy frameworks to guide the development of secure, cost-effective, and equitable telementoring platforms to advance global surgical education.

Deployment of quality-of-service (QoS) based value-added services in IP networks necessitates the use of traffic engineering. Traffic engineering allows service providers to use the network resources efficiently, according to the different quality levels associated with the range of services they offer. Traffic engineering relies typically on monitoring data for both “offline proactive” and “dynamic reactive” approaches. Monitoring data may be used for network provisioning, dynamic resource allocation, route management, and in-service performance verification for value-added IP services. A monitoring system should scale with the network size, the network speed, and the number of customers subscribed to use value-added IP services. This paper investigates the requirements of scalable monitoring system architectures, proposes principles for designing such systems and validates these principles through the design and implementation of a scalable monitoring system for traffic engineering and QoS delivery in IP Differentiated Services networks. Methods for assessing the relative merits of such monitoring systems are proposed. Experimental assessment results prove the scalability, accuracy, and also demonstrate the benefits of the proposed monitoring system.

Computer system for the selection of trenchless and conventional methods for underground utilities

With the extensive growth in terms of data and AI adoption across various fields such as finance, healthcare and insurance, data security and privacy have become significant barriers to innovation. This paper provides a privacy-aware framework for distributed AI as a possible solution which is integrated with cloud-native architectures. By leveraging decentralized model training without sharing raw data, this solution offers a compliant and secure framework for deploying machine learning at scale. A scalable and cost-effective system architecture is proposed that aligns with data protection regulations while maintaining high performance and model accuracy. This approach empowers organizations to leverage AI responsibly, unlocking the potential of sensitive data without compromising privacy.

The significance of on-board spaceborne SAR imaging processing is expatiated first. By analyzing the characteristics of spaceborne SAR imaging processing, a dual — layer, scalable, reconfigurable and fault-tolerant on-board spaceborne SAR real-time imaging processing system architecture, which the top layer adopts data partition parallel processing and the bottom layer adopts multi processors fine granularity close coupling, is put forward. System running efficiency is pointed out to analyze the whole system's efficiency; two system bottom realizable structures of bus sharing and bus distributing were designed.

Industrial ventilation systems are highly susceptible to vibration-induced failures, which can compromise operational efficiency and safety. This paper proposes a predictive maintenance (PdM) framework leveraging vibration-based condition monitoring, integrated with modern data analytics and IoT technologies. We analyze and compare conventional and advanced signal processing techniques, including Fast Fourier Transform, Wavelet, and Cepstrum analysis, as well as machine learning models for fault classification and Remaining Useful Life (RUL) prediction. A simulated testbed based on benchmark datasets (e.g., PRONOSTIA) was used to evaluate model performance. Among various algorithms tested, Random Forest demonstrated the highest accuracy in fault detection. The study further incorporates edge computing, LoRaWAN, and wireless sensor networks (WSNs) into a scalable PdM system architecture. This approach enables real-time vibration monitoring and decision-making to preempt component-level failures. The results show the feasibility of an integrated, intelligent PdM solution for improving equipment lifespan and reducing unplanned downtime in industrial ventilation systems.

Sequential network simulation is a high time-consuming application, and with the emergence of global multihop networks and gigabit-per-second links is becoming a challenging problem. A new approach to this open problem is presented, based on HW/SW co-design. A complete modular and scalable reconfigurable system architecture is explained. Most important features of this simulation framework are: (1) efficient and flexible network simulation, and (2) transparent use of the reconfigurable system by telecommunication networks engineers because of the use of a high level network modeling language.

This paper presents the results of welding tests performed with a 1kW blue laser to determine the power and spot characteristics necessary to be able to perform all the welds required in a battery and battery pack. The results of this study indicate that a minimum of 1,500 Watts with a nominal beam parameter product of 11 mm-mrad is required to interface with a scanner and achieve all of the welds required by this application. Several other key industrial applications are discussed, and the welding performance is characterized for various materials. These results are used to define the optimum laser system parameters to serve a broad range of applications. A new blue laser product architecture is presented that is capable of scaling to multi-kW power levels in order to meet the target performance. The building block for this architecture is a 400-Watt blue laser module. These units can be combined to produce systems of any power level in 400- Watt increments with excellent beam quality. The 1,500-Watt product being developed has a beam parameter product of less than 17 mm-mrad.

Following the advancements in materials development for electrochemical systems, flowable suspension-type (i.e. semi-solid) electrodes have gained much attraction in the last decade, offering scalability and flexible design for many conventional energy storage technologies. Flowable suspension electrodes are multi-phase mixtures of liquid electrolytes and electrochemically active solid particles that can offer fluidization of traditional electrode chemistries and allow adaptation of highly scalable flow-assisted system architectures for large-scale energy storage applications. In suspension electrodes, volume spanning networks of conducting particle clusters facilitate the transport of charges under dynamically changing conditions and the liquid electrolyte phase is responsible for the physical transport of ions and the solid particles. Due to their complex nature, experimental and theoretical approaches are required from a host of disciplines to fully investigate the governing properties of suspension electrodes. Despite its significance, optimizing the electrochemical and rheological parameters not only for efficient electrochemical performance but also for low-dissipation flow remains to be one of the least explored areas. This thesis aims to address the existing gap in rheological understanding of capacitive suspensions in relation to their electrochemical performance and focuses on establishing key structure-property relationships that can ultimately help us achieve low-dissipation flow, high electrochemical performance capacitive flowable systems. This is accomplished through systematic experimentation to establish a link between key rheological properties and the electrochemical performance. Multi-component carbon suspensions consisting of high-surface-area porous carbons and conducting carbon nanoparticles are used as model systems to investigate the effect of particle morphology on charge percolation and rheological behavior of flowable suspension electrodes. A systematic study focusing on different types of slurry preparation protocols is carried out to determine the impact of mixing on electrical conductivity and electrochemical performance of capacitive suspensions. Alternative electroactive materials with hydrophilic surface groups such as MXenes are explored, investigating the potential of clay-like conducting particles for low-dissipation flow FE applications. Lastly, focusing on minimizing rheology dependent losses (e.g. pumping) and improving the power output of low-viscosity suspensions, the emphasis is given to alternative approaches to flow cell design and an overview of porous electrode integration to slurry electrode systems is provided.

Data in the educational context are becoming increasingly important in decision-making and teaching-learning processes. Similar to the industrial context, educational institutions are adopting data-processing technologies at all levels. To achieve representative results, the processes of extraction, transformation and uploading of educational data should be ubiquitous because, without useful data, either internal or external, it is difficult to perform a proper analysis and to obtain unbiased educational results. It should be noted that the source and type of data are heterogeneous and that the analytical processes can be so diverse that it opens up a practical problem of management and access to the data generated. At the same time, ensuring the privacy, identity, confidentiality and security of students and their data is a “sine qua non” condition for complying with the legal issues involved while achieving the required ethical premises. This work proposes a modular and scalable data system architecture that solves the complexity of data management and access. On the one hand, it allows educational institutions to collect any data generated in both the teaching-learning and management processes. On the other hand, it will enable external access to this data under appropriate privacy and security conditions.