Flexible Network Research Articles

DBlock-RLB: An energy efficient framework for intelligent routing and trading based load balancing in SDWSN environment

Wireless Sensor Networks (WSNs) is an emerging field that enables them to work in complex scenarios with enough flexibility. The WSN systems are improved in terms of network flexibility by integrating Software Defined Network (SDN) to obtain SDWSN environment. However, security and energy efficiency are the major issues in the SDWSN environment which leads to low network lifetime. To resolve the existing issues, we proposed DAG-based BLOCKchain-Routing and Load Balancing Model (DBLOCK-RLB). The proposed approach performs routing and load balancing in the SDWSN environment in an energy efficient manner with high security. The SDWSN environment is composed of four major planes as data plane, switch plane, control plane, and application plane. The sensor nodes in the data plane are authenticated priory to ensure legitimacy and reduce unwanted network traffics by adopting Camellia Encryption Algorithm (CAE) which provides secret key to the user by considering several metrics. The authenticated sensor nodes are clustered to reduce energy consumption using Adaptive Threshold based Network Partitioning in which the circular SDWSN environment is split into equal parts and optimal Cluster Head selection is done by considering clustering metrics. From the clustered network, the CH is responsible for intelligent routing. For intelligent routing, we utilize Dual Agent-Twin Delayed Deep Deterministic Policy Gradient (DA-TD3) in which one agent for optimal forwarder selection and another agent for optimal route selection. After routing, the load balancing is done to reduce the network overhead and scalability issues. The proposed work performs trading based load balancing by utilizing Stackelberg Game Model in which all the local controllers are considered as followers and global controller is considered as leaders. All the transactions are stored in the DAG blockchain to reduce the scalability and Proof-of Authentication (PoAH) used to verify the transactions. The proposed work is simulated using NS-3.26 simulation tool and compared with several existing works by validation metrics like throughput at 2.55(mbps), latency at 27.1(ms), packet delivery ratio at 91.5(%), network Lifetime at 546.5(s), and energy consumption at 14.5 (J). The results show that the proposed work outperforms better than the existing works.

Double physical network hydrogels with rapid self-recovery and multistimuli-responsive shape memory effects based on low-methoxyl pectin and host-guest interactions

Double physical network (DPN) hydrogels exhibit superior performance compared with other hydrogel types due to their unique crosslinking structure. However, certain mechanical properties, such as self-recovery and fatigue resistance, still need improvement, and stimuli-responsiveness can be expanded for a wider range of applications. In this study, we presented a facile strategy for designing multifunctional DPN hydrogels based on a poly-cyclodextrin (PCD) and adamantane (Ad) host-guest supramolecular crosslinked polyacrylamide (PAAm) network and a low-methoxyl pectin (LMP) network established through Ca2+ or H+. ‘Egg box’ junction zones between Ca2+ and COO− on the LMP chains, as well as hydrogen bonding and hydrophobic association junction zones, could be formed during this process. The synergistic effect of the flexible supramolecular network and the stiff LMP network imparted remarkable stress tolerance, fatigue resistance, ductility, and anti-piercing capacities to the DPN hydrogels. The thermal reversible DPN structure also endowed the hydrogel with thermal-accelerated rapid self-recovery, as the hydrogel can virtually recover to its original state within 40 min at 80 °C. Multistimuli-responsive including chemical- and thermal-induced shape memory behavior was achieved based on the versatile LMP network. The temporary shape of the LMP hydrogel could be fixed with the introduction of Ca2+ or H+, while spontaneous shape recovery was observed under near infrared (NIR) radiation or immersion in K2CO3 or NaOH solutions, respectively. Additionally, the DPN hydrogels exhibited notable self-healing behavior when the cut surfaces were contacted and stored at 80 °C. More importantly, green natural resources derived raw materials of cyclodextrin (CD) and LMP also offered a sustainable strategy to prepare functional hydrogels. The combination of the above features of the LMP DPN hydrogel provided a new method for designing smart materials with ideal functions for soft actuators and biomedical applications.

Modeling brain network flexibility in networks of coupled oscillators: a feasibility study

Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain’s network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.

Injectable and In Situ Formed Dual-Network Hydrogel Reinforced by Mesoporous Silica Nanoparticles and Loaded with BMP-4 for the Closure and Repair of Skull Defects.

Bone defects are a common and challenging orthopedic problem with poor self-healing ability and long treatment cycles. The difficult-to-heal bone defects cause a significant burden of medical expenses on patients. Currently, biomaterials with mechanical stability, long-lasting action, and osteogenic activity are considered as a suitable way to effectively heal bone defects. Here, an injectable double network (DN) hydrogel prepared using physical and chemical cross-linking methods is designed. The first rigid network is constructed using methylpropenylated hyaluronic acid (HAMA), while the addition of chitosan oligosaccharide (COS) forms a second flexible network by physical cross-linking. The mesoporous silica nanoparticles (MSN) loaded with bone morphogenetic protein-4 (BMP-4) were embedded into DN hydrogel, which not only enhanced the mechanical stability of the hydrogel, but also slowly released BMP-4 to achieve long-term skull repair. The designed composite hydrogel showed an excellent compression property and deformation resistance. In vitro studies confirmed that the HAMA/COS/MSN@BMP-4 hydrogel had good biocompatibility and showed great potential in supporting proliferation and osteogenic differentiation of mouse embryo osteoblast precursor (MC3T3-E1) cells. Furthermore, in vivo studies confirmed that the DN hydrogel successfully filled and closed irregular skull defect wounds, effectively promoted bone regeneration, and significantly promoted bone repair compared with the control group. In addition, HAMA/COS/MSN@BMP-4 hydrogel precursor solution can quickly form hydrogel in situ at the wound by ultraviolet light, which can be applied to the closure and repair of wounds of different shapes, which provides the new way for the treatment of bone defects.

Efficient Traffic Control Using Graph Theory: A Comprehensive Overview and Application

The use of graph theory in the area of traffic control is presented in-depth in this article. Urban transport networks have recurring difficulties with congestion, safety, and traffic efficiency. Graph theory offers a methodical and practical solution to these issues. We examine the fundamental ideas of graph theory in further detail, including its applicability to transportation networks and the many kinds of graphs that are employed in traffic control, including their static and dynamic performance. The article shows how graphical algorithms may be used to improve traffic management, including the shortest path method, flow optimisation, and traffic light synchronisation. Furthermore, we show the usefulness of graph theory in smart city traffic light synchronisation, public transit routing, incident management, and ITS integration through case studies and real-world applications. Data collecting, scalability, and the influence of autonomous cars are just a few of the difficulties and future trends in this sector that are highlighted. In conclusion, this study emphasises the crucial part that graph theory plays in raising transportation efficiency and safety. Its incorporation into traffic control systems holds the possibility of creating more flexible and responsive transport networks, enhancing urban sustainability.

Tracing, Ranking and Valuation of Aggregated DER Flexibility in Active Distribution Networks

Tracing, Ranking and Valuation of Aggregated DER Flexibility in Active Distribution Networks

ENHANCING VEHICULAR NETWORKS WITH DEEP RADIAL BASIS FUNCTION FOR INTELLIGENT TRAFFIC MANAGEMENT

The vehicular networks has spurred research into intelligent traffic management systems to alleviate congestion and enhance safety. However, existing approaches often face challenges in capturing the complex dynamics of urban traffic flow efficiently. In this study, we propose an innovative framework integrating Deep Radial Basis Function (DRBF) networks into vehicular networks for intelligent traffic management. Our approach aims to address the limitations of conventional methods by leveraging the representational power of deep learning while incorporating the flexibility of radial basis function networks. The problem addressed in this research lies in the inadequacy of traditional traffic management systems to adapt to the dynamic nature of urban traffic flow. Existing methods often rely on simplistic models or predefined rules, which may fail to capture the intricate patterns and interactions among vehicles on the road. Consequently, these systems may struggle to provide real-time and accurate traffic management solutions, leading to increased congestion and safety hazards. To bridge this research gap, we propose the integration of DRBF networks, which offer a unique combination of deep learning capabilities and radial basis function interpolation. This hybrid architecture enables the model to learn complex spatial and temporal dependencies from vehicular network data while maintaining computational efficiency and interpretability. By training the DRBF network on historical traffic data and real-time sensor inputs, our methodology can effectively predict traffic flow, identify congestion hotspots, and optimize route recommendations in urban environments. Experimental results on real-world traffic datasets demonstrate the effectiveness of the proposed approach in enhancing traffic management performance. Compared to traditional methods, our DRBF-based framework achieves higher accuracy in traffic flow prediction and generates more efficient routing strategies, leading to reduced travel times and improved overall traffic conditions.

Robust Operation of Flexible Distribution Network With Large-Scale EV Charging Loads

The increasing integration of electric vehicles (EVs) significantly challenges the efficient operation of distribution networks. With the rapid development of power electronic devices represented by soft open point (SOP), flexible distribution networks (FDNs) provide a promising opportunity for integrating large-scale EVs. In this paper, a robust operation method is proposed to improve the economy of EVs and ensure the flexible operation of FDN. First, an EVs-aggregator (EVs-AGG) model is formulated to incorporate diverse charging behaviors. The EVs-AGG is efficiently integrated into FDN through the DC link of multi-terminal SOP. Then, optimal operation strategies are obtained by coordinating EVs charging/discharging power with the regulation of multi-terminal SOP. Furthermore, considering the uncertainties of EVs and photovoltaics, a robust operation framework is established to deal with uncertainties. Finally, case studies based on a practical distribution network indicate that through the interactive power regulation between multi-terminal SOP and EVs-AGG, the proposed method alleviates the negative impacts caused by large-scale EVs integration and improves the operational flexibility of FDN.

Dopamine-driven functional network dynamics: Unraveling network flexibility in Parkinson's disease

Dopamine-driven functional network dynamics: Unraveling network flexibility in Parkinson's disease

Interaction network rewiring and species' contributions to community-scale flexibility.

The architecture of species interaction networks is a key factor determining the stability of ecological communities. However, the fact that ecological network architecture can change through time is often overlooked in discussions on community-level processes, despite its theoretical importance. By compiling a time-series community dataset involving 50 spider species and 974 Hexapoda prey species/strains, we quantified the extent to which the architecture of predator-prey interaction networks could shift across time points. We then developed a framework for finding species that could increase the flexibility of the interaction network architecture. Those "network coordinator" species are expected to promote the persistence of species-rich ecological communities by buffering perturbations in communities. Although spiders are often considered as generalist predators, their contributions to network flexibility vary greatly among species. We also found that detritivorous prey species can be cores of interaction rewiring, dynamically interlinking below-ground and above-ground community dynamics. We further found that the predator-prey interactions between those network coordinators differed from those highlighted in the standard network-analytical framework assuming static topology. Analyses of network coordinators will add a new dimension to our understanding of species coexistence mechanisms and provide platforms for systematically prioritizing species in terms of their potential contributions in ecosystem conservation and restoration.

Multilayer ordered silver nanowire network films by self‐driven climbing for large‐area flexible optoelectronic devices

AbstractSilver nanowire (AgNW) networks hold great promises as next‐generation flexible transparent electrodes (FTEs) for high‐performance flexible optoelectronic devices. However, achieving large‐area flexible AgNW network electrodes with low sheet resistance, high optical transmittance, and a smooth surface remains a grand challenge. Here, we report a straightforward and cost‐effective roll‐to‐roll method that includes interface assembly/wetting‐induced climbing transfer, nanowelding, and washing processess to fabricate flexible ordered layered AgNW electrodes with high network uniformity. By manipulating the stacking number of the interfacially assembled AgNW monolayer, we can precisely tailor and balance the transparency and the conductivity of the electrodes, achieving an exceptional Figure of Merit (FoM) value of 862. Moreover, the ordered layered structure enhances surface smoothness, compared with randomly arranged structures. To highlight the potential of these ordered layered AgNW network electrodes in flexible optoelectronic devices, we successfully employ them as highly sensitive strain sensors, large‐area flexible touch screens, and flexible smart windows. Overall, this work represents a substantial advance toward high‐performance FTEs over large areas, opening up exciting opportunities for the development of advanced optoelectronic devices.image

A Novel Approach to Dynamic Line Rating Calculation

In order to adjust the electric power network to today’s trends—such as the charging of electric vehicles, design of the Internal Electricity Market (IEM), headway of renewable energy sources, etc.—the demand for flexible network solutions is increasingly significant. Dynamic line rating (DLR) technology offers a flexible solution for the uprating of overhead lines. Moreover, the DLR methodology can be used in a wider system approach in order to establish a line management system, including functions such as ice prevention subsystems or the real-time tracking of clearances. Therefore, the extension possibilities and operational features of DLR-based systems have been extensively researched in the last few years, which is also confirmed by the high number of pilot projects funded for research and development on the topic of DLR. The key question during the implementation of a DLR system is the obtainable operational safety of such a system. This mainly depends on the installation places of the field equipment and the accuracy of the line rating calculation models. The aim of this article is the comparison of the so far available international line rating calculation models and also to propose another way for the determination of the real-time line rating. Moreover, laboratory measurements and case studies are presented for the confirmation of the proposed model’s reliability.

Flexible polymer networks: rubber elasticity and segmental orientation

Abstract Thermoreversible networks are very attractive materials because of a large variety of suitable thermoreversible chemical bonds which can be used for crosslinking. In future, to certain extent, they may replace so far used irreversibly crosslinked networks and contribute to more sustainable material concepts also in the field of crosslinked elastomers. This paper gives some theoretical basis for understanding the elastic deformation properties of crosslinked polymers and presents some experimental tools for studying deformation behavior not only on a macroscopic length scale but also on a segmental scale. Basic knowledge of these two topics is useful for chemists and material scientists when designing new sustainable elastomeric polymer networks.

Network Slicing in 5G Systems: Challenges, Opportunities and Implementation Approaches

The introduction of the fifth-generation (5G) of network technology has radically transformed the telecommunications landscape by providing high-speed, low-latency communication suitable for a range of innovative applications. However, this transformation also introduces novel network complexity and resource management challenges. An emerging solution to these formidable challenges is 'Network Slicing,' a powerful technology that plays a crux role in the efficient management of 5G network systems. Network slicing, enabled by key technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), allows the creation of multiple virtual and independent networks operating on a shared physical infrastructure. This ability contributes to a more flexible, scalable, and efficient network system, making it aptly suited for diverse 5G applications. In this paper, we conduct an in-depth examination of network slicing in 5G systems, its implementation strategies, associated challenges, and potential solutions. Two real-world case studies underline its practical applications, while a discussion on the future outlook anticipates advances in AI and ML to refine network slicing management. The paper posits that while network slicing brings its own set of complexities, its continuous evolution and relentless innovations gear towards overcoming such challenges, paving the way to a future of 5G networking marked by versatility, reliability, and efficiency of unprecedented levels.

Coordinated control strategy of reactive power compensation based on a flexible distribution network transformer

In order to solve the problem of the power quality caused by distributed power access to the distribution network, this paper proposes a coordinated control strategy of reactive power compensation based on a flexible distribution transformer. First, the working principle of the flexible distribution transformer is analyzed, and the mathematical model of energy acquisition and the regulation converter of the flexible distribution transformer are studied as well. The device-level control strategies of energy acquisition and regulation converters are proposed, respectively. Then, in order to maintain the stability of the bus voltage and quickly respond to the reactive power changes of the system, a coordinated control strategy for the reactive power compensation of flexible distribution transformers is proposed. The priority herein is to maximize the reactive power compensation capacity of the energy harvesting converter. When the energy harvesting converter reaches the compensation upper limit, the control converter is used for reactive power compensation to further suppress the grid voltage fluctuation. Finally, it is verified through simulations that the flexible distribution transformer can realize the reactive power compensation of the distribution network and effectively improve the power quality of the distribution network.

Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions

In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that includes time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the full hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible framework for data-driven modeling of single event survival outcomes that estimates time-varying effects and a complex baseline hazard by design. An R package is available at https://github.com/Jesse-Islam/cbnn.

Integration of curtailed wind into flexible electrified heating networks with demand-side response and thermal storage: Practicalities and need for market mechanisms

Increasingly high renewable penetration in electrified energy systems causes increasing levels of curtailment that must eventually be mitigated. System flexibility, energy storage and demand-side response in the form of flexible heating and cooling demands are efficient solutions for curtailment mitigation, however challenges remain such as the economic benefits of energy storage and the financial impacts of curtailment mitigation on consumers, among others. In this paper the mitigation of curtailed wind energy, incentivised with discount payments, through flexible electrified heating networks with demand-side response and thermal energy storage is investigated with a genetic optimisation algorithm, and results are analysed in terms of overall costs, technical feasibility and level of curtailed wind use from the perspective of the network operator and consumer. It is found that large-scale thermal storage lowers the cost and increases levels of curtailed wind integration, and allows district heating networks to run almost entirely on otherwise curtailed wind energy (up to 97.0 % in the example considered here). However without adequate market mechanisms, demand-side response is financially beneficial to all stakeholders only at low levels; with a split cost approach, financial incentives or market mechanisms are necessary for higher levels of, and more efficient, wind curtailment mitigation.

Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources

This paper presents the energy management of smart distribution network including integrated system of hydrogen storage and renewable sources. Objective is to assess economic, operation, flexibility, and reliability goals of the distribution system operator. Objective function minimizes costs of operation, reliability, energy losses, and network flexibility. Scheme is constrained by AC optimal power flow equations, network reliability limitation, and integrated system model. Scheme utilizes scenario-based stochastic optimization to model of uncertainties, such as load, parameters of renewable resources, energy prices, and the availability of network equipment. Novelty is modeling and performance evaluation of the proposed integrated system as a type of flexibility resource in the operation of distribution systems proportional to the economic, operation, flexibility, and reliability objectives of the network operator. Numerical results demonstrate energy management capabilities of the discussed integrated energy system, which contribute to enhancing economic and technical conditions of distribution network. The inclusion of hydrogen storage in the renewable integrated energy system has been found to enhance the economic viability, operational efficiency, and reliability of the distribution network by around 46.8%, 41%–53%, and 95%, respectively, when compared to network power flow. Aforementioned integrated system demonstrates 100% flexibility through the utilization of hydrogen storage.

Collagen Networks under Indentation and Compression Behave Like Cellular Solids.

Simple synthetic and natural hydrogels can be formulated to have elastic moduli that match biological tissues, leading to their widespread application as model systems for tissue engineering, medical device development, and drug delivery vehicles. However, two different hydrogels having the same elastic modulus but differing in microstructure or nanostructure can exhibit drastically different mechanical responses, including their poroelasticity, lubricity, and load bearing capabilities. Here, we investigate the mechanical response of collagen-1 networks to local and bulk compressive loads. We compare these results to the behavior of polyacrylamide, a fundamentally different class of hydrogel network consisting of flexible polymer chains. We find that the high bending rigidity of collagen fibers, which suppresses entropic bending fluctuations and osmotic pressure, facilitates the bulk compression of collagen networks under infinitesimal applied stress. These results are fundamentally different from the behavior of flexible polymer networks in which the entropic thermal fluctuations of the polymer chains result in an osmotic pressure that must first be overcome before bulk compression can occur. Furthermore, we observe minimal transverse strain during the axial loading of collagen networks, a behavior reminiscent of open-celled cellular solids. Inspired by these results, we applied mechanical models of cellular solids to predict the elastic moduli of the collagen networks and found agreement with the moduli values measured through contact indentation. Collectively, these results suggest that unlike flexible polymer networks that are often considered incompressible, collagen hydrogels behave like rigid porous solids that volumetrically compress and expel water rather than spreading laterally under applied normal loads.

Enhanced Strength for Double Network Hydrogel Adhesive Through Cohesion‐Adhesion Balance

AbstractHydrogel adhesives exhibit great potential in various biomedical fields such as tissue sealing and soft robotics. However, the high‐water content and defective network structures of these hydrogel adhesives result in low intrinsic mechanical strength, severely impeding their application. In this study, it is reported that the strong hydrogel adhesive strength can be achieved when a balance is established between adhesive forces and cohesive forces. Based on this principle, a new double network (DN) design is created to combine an adhesive polyvinyl alcohol (PVA) as the first network with a flexible sodium alginate (SA) as the second network. A delicate balance is achieved between cohesion and adhesion by adjusting the ratio between the first rigid adhesive network and the second flexible network. As a result, this balanced DN hydrogel adhesive exhibits a strong tissue adhesion strength, approximately three times higher than that in the non‐balance situation.