Photonic integrated technologies for future radio-over-fiber systems

Integrated optical waveguide amplifiers, with their compact footprint, low power consumption, and scalability, are the basis for optical communications. The realization of high gain in such integrated devices is made more challenging by the tight optical constraints. In this work, we present efficient amplification in an erbium–ytterbium-based hybrid slot waveguide consisting of a silicon nitride waveguide and a thin-film active layer/electron-beam resist. The electron-beam resist as the upper cladding layer not only possesses the role of protecting the waveguide but also has tighter optical confinement in the vertical cross-section direction. On this basis, an accurate and comprehensive dynamic model of an erbium–ytterbium co-doped amplifier is realized by introducing quenched ions. A modal gain of above 20 dB is achieved at the signal wavelength of 1530 nm in a 1.4 cm long hybrid slot waveguide, with fractions of quenched ions fq = 30%. In addition, the proposed hybrid waveguide amplifiers exhibit higher modal gain than conventional air-clad amplifiers under the same conditions. Endowing silicon nitride photonic integrated circuits with efficient amplification enriches the integration of various active functionalities on silicon.

SummaryNetwork slicing (NS) presents the key enabler of cellular network improvements. It allows enhancing the performance of diverse requirements supported for verticals industries. The concept of NS was carefully studied over the previous few years, and the primary operational principles were developed. However, there is an important need for more investigations on studying NS to enable further development. This article offers a deep study related to the NS principle, the recent standardization process for Third Generation Partnership Project and Fifth Generation Public Private Partnership, the diverse broad use cases, NS key concepts, NS architectures, and NS management and orchestration. Besides, it discusses radio access network slicing and sharing, the algorithms, the projects, and the NS practical experience and practices. Finally, this article proposes and highlights a possible solution to several open research issues.

The IPv6 routing protocol for low-power and lossy networks (RPL) provides the multipoint-to-point, point-to-multipoint, and point-to-point routing mechanisms for wireless sensor networks. However, for point-to-multipoint, i.e., multicast from the sink node, the RPL poses only the principles of routing operation, and the practical mechanism is still an open issue. In this paper, we propose a wireless shortest path heuristic (W-SPH) mechanism for constructing a multicast tree which builds effective routing topology for multicast traffic, and as a consequence, yields higher energy efficiency. We also introduce the proactive multicast forwarding with RPL (PMFR), a multicast mechanism with proactive retransmission and early relay, to increase the reliability of the multicast. Our simulation results demonstrate that the PMFR can achieve a significantly higher delivery ratio and a 50% lower delay than previous works. The PMFR also reduces about 40% energy consumption as compared to the multicast protocol for low-power and lossy networks (MPL).

Introduction Video-on-demand (VoD) is an enabling technology [1] allowing geographically distributed users to interactively access video files from remote VoD servers. With this technology, users can playback the video content without waiting for the entire file to arrive and also enjoy the flexible control of video playback with VCR-like functions. Compared with conventional data communications, delivery of video data has more stringent requirements on network bandwidth, delay and loss [2]. However, the current Internet is inherently a packet-switched network that was not designed to handle isochronous (continuous time-based) traffic such as audio and video. The Internet can only provide best-effort services and has no guarantee on the quality of service (QoS) for audio and video transmission. In addition, a large-scale VoD system must have a large storage capacity to archive hundreds or even thousands of videos and a high performance I/O bandwidth to handle an enormous number of concurrent video streams. Also, in practical situations, clients can connect to the network, says Internet, with different communication technologies such as modem, ASDL and wireless link and their downstream rates may vary from 56 kbps to 10 Mbps or above. To cater for the heterogeneous requirement, the system will deliver different qualities of video streams to the clients according to their capacity constraints. If the clients have a low bandwidth connection such as 56 Kbps, they will receive the videos encoded at a low bit rate. On the other hand, the high quality video will be delivered to the customers having the broadband access capability. As a result, there are still many open issues in designing protocols and transmission strategies for VoD systems in such environment. There are three main approaches for building a large scale VoD system in heterogeneous network environment in a cost-efficient way. The first one is based on the hierarchical architecture. The second one makes use of peer-to-peer (P2P) technology to support video services. The third one is a hybrid approach that combines with the use of the first two approaches.

Many applications in engineering and physical sciences involve turbulent flows interacting with shock waves. High-speed flows around aerodynamic bodies and through propulsion systems for high-speed flight abound with interactions of shear driven turbulence with complex shock waves. Supernova explosions and implosion of a cryogenic fuel pellet for inertial confinement fusion also involve the interaction of shockwaves with turbulence and strong density variations. Numerical simulations of such physical phenomena impose conflicting demands on the numerical algorithms. Capturing broadband spatial and temporal variations in a turbulent flow suggests the use of high-bandwidth schemes with minimal dissipation and dispersion, while capturing the flow discontinuity at a shock wave requires numerical dissipation.Results from three promising shock-capturing schemes a) high order WENO, b) nonlinear artificial diffusivity with compact finite differences, and c) a hybrid approach combining high-order central differencing with WENO near the shocks are compared using the Taylor-Green problem and compressible isotropic turbulence with eddy-shocklets. The performance of each scheme is characterized in terms of an effective bandwidth. The comparison highlights the damaging effect of numerical dissipation when the WENO scheme is applied everywhere. The hybrid approach is found to be best suited for studying shock-turbulence interactions.Results from previous DNS and LES studies of the canonical shock-turbulence interaction problem, i.e. the interaction of isotropic turbulence with a (nominally) normal shock and comparison with available theory and experimental data are recalled. The principal physical effects include the amplification of turbulent kinetic energy across the shock and its anisotropy, change in turbulence length scales across the shock, departure from the common assumption of strong Reynolds analogy (used in modeling turbulence in high-speed flows), and the distortion of the shock due to its interaction with the incident turbulence. In this context new results from our SciDAC sponsored project are shown and open issues are mentioned. New DNS results achieve a significantly larger turbulence Reynolds number and allow an exploration of the nonlinear effects. While the linear interaction theory of Ribner provides useful estimates of the amplification of vorticity fluctuations across the shock, it misses the strong nonlinear dynamics of the energized and highly anisotropic vorticity downstream of the shock. It is found that previous DNS studies also underestimated this effect. The simulations show that turbulent self-stretching and tilting mechanisms bring about a relatively rapid return to isotropy in the turbulent vorticity field. The turbulent velocity field, however, does not show any appreciable tendency towards isotropy. It is further observed that when the turbulence interacting with the shock is sufficiently energetic the instantaneous shock structure is significantly modified; local regions of significant over-compression are found as well as regions where the mean shock compression is nearly isentropic. Estimates for the computational resources necessary for studying this fundamental shock-turbulence interaction problem at higher Reynolds number on peta-scale computing systems are given.

A hybrid approach with agent-based simulation and clustering for sociograms

Beacon or safety messages are broadcasted by Vehicular Ad hoc Networks (VANETs) to disseminate network state or emergency incident information to other vehicles in the network. The freshness of information depends upon the frequent transmission of beacons. Similarly, to increase the awareness area or communicating with the distant nodes, beacons are transmitted with at high transmission power. However, increasing the beacon rate or transmission power has negative effects on information, communication efficiency because of the finite wireless link bandwidth. Therefore, different schemes have been proposed to individually control beacon's transmission power, transmission rate, or contention window at the MAC layer, or combination of those, to achieve quality beacon communication in VANETs. The latter case is called hybrid adaptive beaconing schemes. In literature, there are many hybrid adaptive beaconing schemes that control multiple communication parameters to efficiently broadcast beacon messages in VANETs. In this paper, we briefly summarize various and qualitatively compare various aspects of those schemes. The open and challenging issues are also highlighted in this paper.

Flexgrid technology is now considered to be a promising solution for future high-speed network design. In this context, we need a tutorial that covers the key aspects of elastic optical networks. This tutorial paper starts with a brief introduction of the elastic optical network and its unique characteristics. The paper then moves to the architecture of the elastic optical network and its operation principle. To complete the discussion of network architecture, this paper focuses on the different node architectures, and compares their performance in terms of scalability and flexibility. Thereafter, this paper reviews and classifies routing and spectrum allocation (RSA) approaches including their pros and cons. Furthermore, various aspects, namely, fragmentation, modulation, quality-of-transmission, traffic grooming, survivability, energy saving, and networking cost related to RSA, are presented. Finally, the paper explores the experimental demonstrations that have tested the functionality of the elastic optical network, and follows that with the research challenges and open issues posed by flexible networks.

Early detection of cancer significantly improves patient outcomes, with biomarkers offering a promising avenue for earlier and more precise diagnoses. Microfluidic biosensors have emerged as a powerful tool for detecting these biomarkers in body fluids, providing enhanced sensitivity, specificity, and rapid analysis. This review focuses on recent advances in microfluidic biosensors from 2018 to 2024, detailing their operational principles, fabrication techniques, and integration with nanotechnology for cancer biomarker detection. Additionally, we have reviewed recent innovations in several aspects of microfluidic biosensors, such as novel detection technologies, nanomaterials and novel microfluidic chip structures, which significantly enhance detection capabilities. We highlight key biomarkers pertinent to early cancer detection and explore how these innovations in biosensor technology contribute to the evolving landscape of personalized medicine. We further explore how these technologies could be incorporated into clinical cancer diagnostic workflows to improve early detection and treatment outcomes. These innovations could help enable more precise and personalized cancer diagnostics. In addition, this review addresses several important issues such as enhancing the scalability and sensitivity of these biosensors in clinical settings and points out future possibilities of combining artificial intelligence diagnostics with microfluidic biosensors to optimize their practical applications. This overview aims to guide future research and clinical applications by addressing current challenges and identifying opportunities for further development in the field of biomarker research.

Shaping the stationary energy storage landscape with reversible fuel cells

Airbags are currently being evaluated by NASA Langley Research Center (LaRC) as a candidate impact attenuation system technology for earth landing of the Orion crew exploration vehicle (CEV). The purpose of the system is to limit landing loads and provide stability, to protect the crew and to allow vehicle reuse. Other candidate technologies include retro-rockets, crushables, and hybrid approaches. In support of LaRC's investigation, ILC Dover has generated a conceptual design of an airbag landing system (ALS) for a generic CEV and fabricated a prototype airbag set. ILC modeled the system using LS DYNA, and showed that the proposed design meets objectives in response to nominal and off nominal landing scenarios. Presented herein is an overview of airbag principles of operation, key requirements, design drivers, configuration trades, supporting analysis, and a design overview. Materials selection is discussed, along with an overview of planned testing.

Adaptive control is essential for enabling mobile robots to operate autonomously in dynamic and unpredictable environments. Despite extensive research, existing approaches remain fragmented across methodologies, application domains, and evaluation criteria. This review presents a unified taxonomy of five core adaptive control strategies: Model Reference Adaptive Control (MRAC), Fuzzy Logic Adaptive Control (FLAC), Neural Network Adaptive Control (NNAC), Reinforcement Learning-Based Adaptive Control (RLAC), and hybrid approaches. These strategies are examined across four primary robotic domains: autonomous navigation, mobile manipulation, multi-robot coordination, and human-in-the-loop systems. Unlike prior reviews that focus on specific techniques or limited domains, this work provides a comprehensive synthesis of the strengths and limitations of each strategy with respect to adaptability, robustness, computational efficiency, and real-world deployment. Hybrid methods exhibit strong potential due to their flexibility but often introduce increased design complexity and implementation challenges. Key open issues include the lack of standardized performance metrics, limited validation on physical hardware, and restricted generalizability to unstructured environments. Addressing these challenges requires interdisciplinary collaboration across robotics, control, artificial intelligence, embedded systems, and human–robot interaction. By linking control strategies to practical applications, this review supports the development of adaptive robotic systems that are scalable, explainable, energy-efficient, and responsive to human behavior.

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

The purpose of this research is to systematize contemporary high-precision geodesy methods and assess their efficiency for monitoring Earth surface deformations. The study reviews operational principles and technical characteristics of Global Navigation Satellite Systems (GNSS), Continuously Operating Reference Station (CORS) networks, Interferometric Synthetic Aperture Radar (InSAR) techniques, and hybrid approaches that combine satellite-based and terrestrial measurements. Examples of practical applications are provided for monitoring engineering structures, natural landscapes, and infrastructure facilities, including the integration of Unmanned Aerial Vehicles (UAVs) for photogrammetric surveys. The analysis covers accuracy parameters, temporal resolution, spatial coverage, cost efficiency, and operational constraints of each method. Specific attention is given to error sources such as atmospheric delays, multipath effects, and limitations in dense urban or forested environments. The research proposes a comparative framework for selecting optimal monitoring techniques based on object-specific conditions, required precision, and resource availability. It also identifies trends shaping the future of geodetic monitoring, including the implementation of GNSS Precise Point Positioning in Real Time Kinematic mode (PPP-RTK), artificial intelligence integration for automated deformation detection and classification, multi-sensor data fusion (GNSS, InSAR, LiDAR, photogrammetry), and expanded access to free satellite radar imagery. The study emphasizes the importance of engineering and technical support, covering advanced GNSS receivers, high-precision total stations, digital levels, and specialized processing software for both GNSS and InSAR data. GIS integration is highlighted as a critical step for visualizing and analyzing deformation patterns within unified spatial analytical environments. The findings confirm that combining multiple geodetic techniques significantly increases the reliability and spatial completeness of deformation monitoring, supporting timely decision-making in safety-critical and geodynamically unstable areas.

Algal biorefineries have emerged as a sustainable and versatile platform for the production of bioactive molecules and renewable energy, addressing the growing global demand for environmentally friendly resources. Microalgae and macroalgae offer multiple advantages, including rapid biomass accumulation, high photosynthetic efficiency, minimal land requirement, and the ability to grow in diverse environmental conditions, making them ideal candidates for integrated biorefineries. Recent technological innovations in cultivation systems, including open ponds, photobioreactors, and hybrid approaches, have enhanced biomass productivity while reducing environmental impact. Advanced downstream processing techniques, such as green extraction methods, enzymatic hydrolysis, and supercritical fluid extraction, enable efficient recovery of proteins, polysaccharides, lipids, pigments, and other high-value bioactive compounds. Simultaneously, algal biomass can be converted into renewable energy forms such as biodiesel, bioethanol, biogas, and hydrogen, supporting the transition toward a low-carbon economy. Integrated biorefinery approaches further maximize resource utilization by combining bioactive compound recovery with energy production and waste valorization, promoting circular bioeconomy principles. This review critically examines recent advances in algal cultivation, bioprocessing, and bioactive molecule extraction, highlighting their applications in medicine, nutrition, and sustainable energy production. Challenges related to cost, scalability, and technological feasibility are discussed, along with future prospects for commercial implementation. By bridging fundamental research and industrial application, algal biorefineries represent a promising strategy for sustainable production of bioactive compounds and renewable energy.