Flexible Network Research Articles

Hypertension is associated with reduced resting-state medial temporal lobe dynamic network flexibility in older African Americans.

Hypertension disproportionately affects African Americans and is a risk factor for Alzheimer's disease (AD). We investigated the relationship of blood pressure (BP) with medial temporal lobe (MTL) dynamic network flexibility (a novel AD biomarker) and cognitive generalization in older African Americans. In a cross-sectional study, 37 normotensive (systolic BP <130 mmHg, 82.5% F, 64.4 ± 4.9 years; 14.3 ± 2.1 years of education) versus 79 hypertensive (systolic BP ≥130 mmHg, 79.5% F, 66.8 ± 4.1 years; 14.0 ± 0.2 years of education) participants were enrolled. All participants completed a 10-min resting-state functional magnetic resonance imaging scan to assess MTL dynamic network flexibility and two generalization tasks to assess cognition. Anthropometrics and aerobic fitness (via 6-min walk test) were also determined. There was no difference in BMI (29.7 ± 6.4 vs. 31.9 ± 6.3 kg/m2, p = 0.083) or aerobic fitness (15.5 ± 2.6 vs. 15.1 ± 2.6 mL/kg/min; p = 0.445) between normotensive and hypertensive groups. However, normotensive participants had higher MTL dynamic network flexibility compared to hypertensive participants (0.42 ± 0.23 vs. 0.32 ± 0.25 mL, p = 0.040), and this was associated with higher mean arterial blood pressure (r = -0.21, p = 0.036). Therefore, hypertensive older African Americans demonstrated lower MTL dynamic network flexibility compared to their normotensive counterparts independent of BMI and aerobic fitness. Further studies are required to determine how blood pressure mediates AD risk in African Americans.

DSP-based inter-channel interference monitoring in flexible wavelength-routed networks

The efficiency of optical networks employing flexible wavelength division multiplexing (WDM) can be increased by maximizing the throughput of each individual channel, provided that the position of its neighboring channel is known with sufficient accuracy in order to avoid inter-channel interference. In this paper, we propose a digital signal processing (DSP) algorithm, leveraging the use of an artificial neural network (ANN), to estimate the neighboring channels’ distance by processing raw digital samples from a standard coherent receiver. We present an efficient dataset design approach, based on Latin hypercube sampling (LHS), in order to effectively optimize and validate the algorithm under different assumptions on the optical WDM channels. We investigate the accuracy of the ANN-based DSP scheme through simulation analysis, highlighting its potential in relation to the characteristics of the optical network. Finally, we validate our approach in an experimental setup using standard commercial coherent transceivers. The experimental results show that the distance from the neighboring WDM channels can be estimated with a root mean square error of less than 1.5 GHz for a channel under test with a symbol rate of 52 GBaud.

Multi-resource dynamic coordinated planning of flexible distribution network

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

Polydopamine-Anchored Cellulose Nanofiber Flexible Aerogel with High Charge Transfer as a Substrate for Conductive Materials.

In order to obtain a flexible aerogel substrate for conductive materials used in the electrode, polydopamine-anchored cellulose nanofiber (PDA@CNF) was introduced into a polyethylene imine-poly(vinyl alcohol) (PEI-PVA) cross-linking network which used 4-formylphenylboronic acid (4FPBA) as bridge. The incorporation of rigid CNF as a structural scaffold effectively improved the pore architecture of the aerogel, potentially providing substantial advantages for the infiltration and deposition of conductive materials. Additionally, the outstanding stability and flexibility exhibited by the aerogel in aqueous solutions suggest its significant potential for applications in flexible electrodes. Furthermore, electrochemical experiments showed that the rapid pathway formed between PDA and PEI could enhance the charge-transfer rate within the aerogel substrate. It is anticipated that such an enhancement would significantly benefit the electrochemical attributes of the electrode. Inspired by mussels, our introduced PDA-anchored rigid CNF into flexible polymer networks to fabricate aerogel substrates for electrode materials. This study would contribute to the development and utilization of flexible electrodes while reducing carbon footprint in energy production and conversion processes.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid

An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.

Understanding the mechanisms underlying cognitive control in psychosis.

Cognitive control (CC) involves a top-down mechanism to flexibly respond to complex stimuli and is impaired in schizophrenia. This study investigated the impact of increasing complexity of CC processing in 140 subjects with psychosis and 39 healthy adults, with assessments of behavioral performance, neural regions of interest and symptom severity. The lowest level of CC (Stroop task) was impaired in all patients; the intermediate level of CC (Faces task) with explicit emotional information was most impaired in patients with first episode psychosis. Patients showed activation of distinct neural CC and reward networks, but iterative learning based on the higher-order of CC during the trust game, was most impaired in chronic schizophrenia. Subjects with first episode psychosis, and patients with lower symptom load, demonstrate flexibility of the CC network to facilitate learning, which appeared compromised in the more chronic stages of schizophrenia. These data suggest optimal windows for opportunities to introduce therapeutic interventions to improve CC.

The Ins and Outs of Homeodomain-Leucine Zipper/Hormone Networks in the Regulation of Plant Development.

The generation of complex plant architectures depends on the interactions among different molecular regulatory networks that control the growth of cells within tissues, ultimately shaping the final morphological features of each structure. The regulatory networks underlying tissue growth and overall plant shapes are composed of intricate webs of transcriptional regulators which synergize or compete to regulate the expression of downstream targets. Transcriptional regulation is intimately linked to phytohormone networks as transcription factors (TFs) might act as effectors or regulators of hormone signaling pathways, further enhancing the capacity and flexibility of molecular networks in shaping plant architectures. Here, we focus on homeodomain-leucine zipper (HD-ZIP) proteins, a class of plant-specific transcriptional regulators, and review their molecular connections with hormonal networks in different developmental contexts. We discuss how HD-ZIP proteins emerge as key regulators of hormone action in plants and further highlight the fundamental role that HD-ZIP/hormone networks play in the control of the body plan and plant growth.

Electrodeposition of polyaniline on reduced graphene oxide/cotton yarn with tunable electrochemical performance for flexible textile supercapacitors

Graphene/cotton fibers show significant promise in wearable energy storage due to their low cost, porous structure, and exceptional integration ability into wearable systems. However, the eco-unfriendly reductants and standalone electric double-layer capacitor hindered their application. Herein, a green and rapid hydrothermal-electrodeposition method was proposed to fabricate polyaniline (PANI) decorated reduced graphene oxide (rGO)/cotton yarns without using any chemical reductants and oxidants. The PANI/rGO/cotton (PRC) yarn exhibited porous conductive network, structural controllability, and mechanical flexibility. Additionally, the PRC yarn electrode delivers a fast electron transport and ion migration, synergistic energy storage contribution, and a controllable capacitance (up to 81.2 mF cm−1 at 0.2 mA cm−1). The assembled yarn supercapacitor shows a good capacitance (19.8 mF cm−1 at 0.08 mA cm−1), excellent energy-power density (2.7 μWh cm−1 at 40 μW cm−1), and great capacitance retention. This green fabrication of PRC yarns brings new insights into the development of wearable energy storage.

Big infrastructure and/as systemic flexibility: The Sites Reservoir story

If constructed as planned, the Sites Reservoir Project will add roughly 1.85 billion m3 (1.5 million acre-feet) of storage capacity to California's water system. Per project proponents, however, the reservoir complex should be understood as infrastructure that would not only increase available water supply but also provide the state's water network with an infusion of a vital systemic quality: flexibility. This article considers such discourses of flexibility through a case study of the Sites project, grounded in analysis of planning documents, government, think tank, and NGO reports, and media coverage from outlets across California. The resulting account shows how the ideal of the flexible system orients efforts to adapt the California water network to the heightened forms of hydrological variability associated with climate change. The analysis traces the emergence of the notion of a "flexibility crisis" plaguing the state's water network and elaborates the temporalities and spatialities characteristic of the systemic flexibility that Sites is understood to enhance. The project of increasing the water network's flexibility through the construction of new large-scale storage infrastructures like Sites is shown to be an undertaking that serves to sustain of many of the system's established (and notably inflexible) consumptive arrangements. The case thus demonstrates the importance of attending to how material properties like flexibility &amp;ndash; commonly glossed as desirable system-scale qualities &amp;ndash; come to be unevenly spatialized within infrastructural networks and across landscapes.

Two-step modification strategy enhances carbon fiber/old corrugated container fiber flexible conductive film

Carbon fiber (CF) possesses inherent properties of low density, high strength, and environmental stability. Its high aspect ratio and knittability enable the formation of flexible conductive networks. Old corrugated container (OCC) is rich in cellulose and hemicellulose, rendering them flexible and exhibiting a low coefficient of thermal expansion. In this paper, CF/OCC/PVA flexible conductive films were prepared using polyvinyl alcohol (PVA) as the matrix, CF as the conductive filler and OCC as the flexible support material. Firstly, the coupling agent was grafted on the surface of CF to enhance their surface activity, improve the dispersion of CF in solution and the chemical bonding of CF to the matrix. Subsequently, sizing nanoparticles were applied to increase the roughness of the CF surface. This served to increase the volume of the conductive network and enhance the mechanical interlocking between CF and PVA. The results show that the two-step combined modification strategy have a significant positive impact on the interfacial chemical activity, contact angle, and roughness of CF. Moreover, it leads to notable improvements in various properties of the conductive film materials. This finding is relevant for the preparation of CF reinforced other matrix materials as well as for expanding the special properties and application value of CF conductive thin film materials.

Simulation study of compressed ultrafast 3D imaging based on interferometry

Observing the 3D (three-dimensional) surface vibration in 100 MHz is challenging due to low speed of CCD/CMOS camera. In order to obtain the ultrafast dynamic 3D shape information, a compressed ultrafast 3D imaging framework based on interferometry is proposed in this paper. Based on this imaging framework, an reconstruction method combining plug and play-fast flexible denoising network is developed to recover high resolution interference fringe patterns and the corresponding 3D information. The simulation results show that the proposed method can observe the dynamic processes of the anti-phase vibration and the in-phase vibration of the crystal oscillator, therefore the dynamic 3D model is established. The proposed method outperforms other reconstruction methods in terms of average peak signal to noise ratio and structural similarity in recovery the ultrafast dynamic 3D surface.

Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach

Radio access network (RAN) slicing can significantly improve network flexibility and resource utilization efficiency. Generally, deep reinforcement learning (DRL) is a prevailing approach to implementing practicable resource management in RAN slicing. However, this demand-aware resource allocation suffers from long training time and slow execution efficiency. This can lead to the inability to quickly achieve the desired spectral efficiency and service level agreement satisfaction rate. To tackle this issue, we propose a novel RAN slicing resource allocation framework based on a deep hierarchical RL framework for efficient resource scheduling. Structurally, our framework consists of a policy selection network and a policy evaluation network. In particular, a newly built action acceleration unit can achieve quick reward accumulation, thus speeding up the optimal policy search. Extensive simulations show that our proposal has higher system utility and faster convergence compared to the state-of-the-art DRL algorithms.

Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on Geospatial Computing Platform

Detecting and monitoring surface deformation using radar satellite data is vital in geohazard assessment. Sentinel-1 has provided unprecedented spatial and temporal resolution, but data processing is complicated and poses computational challenges. Although software and tools exist, each with its own limitations. SNAP-ESA is notable for its user-friendly interface and stable performance in Interferometric Synthetic Aperture Radar (InSAR). However, SNAP-ESA lacks a flexible approach for generating interferometric time series stacks for Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques and faces computational challenges over large areas. Here, we present an automated Python workflow, SNAPWF, using SNAP-ESA to enable efficient PSI and SBAS interferometric time series stacks generation using flexible network graphs. SNAPWF has been implemented on a dedicated geospatial computing platform, enabling efficient performance over large areas. Results confirm its ability to generate PSI and SBAS interferometric stacks using full Sentinel-1 scenes and achieve results comparable to existing software.

Rapid and on-site wireless immunoassay of respiratory virus aerosols via hydrogel-modulated resonators

Rapid and accurate detection of respiratory virus aerosols is highlighted for virus surveillance and infection control. Here, we report a wireless immunoassay technology for fast (within 10 min), on-site (wireless and battery-free), and sensitive (limit of detection down to fg/L) detection of virus antigens in aerosols. The wireless immunoassay leverages the immuno-responsive hydrogel-modulated radio frequency resonant sensor to capture and amplify the recognition of virus antigen, and flexible readout network to transduce the immuno bindings into electrical signals. The wireless immunoassay achieves simultaneous detection of respiratory viruses such as severe acute respiratory syndrome coronavirus 2, influenza A H1N1 virus, and respiratory syncytial virus for community infection surveillance. Direct detection of unpretreated clinical samples further demonstrates high accuracy for diagnosis of respiratory virus infection. This work provides a sensitive and accurate immunoassay technology for on-site virus detection and disease diagnosis compatible with wearable integration.

Review on development prospect of operation scheduling strategies for flexible interconnected distribution network in renewable energy-penetrated power system

As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected.

A Neural Network Approach for Software Reliability Prediction

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction.

3D Printing of Maturable Tissue Constructs Using a Cell‐Adaptable Nanocolloidal Hydrogel

Abstract3D‐printed cell‐laden hydrogels as tissue constructs show great promise in generating living tissues for medicine. Currently, the maturation of 3D‐printed constructs into living tissues remains challenge, since commonly used hydrogels struggle to provide an ideal microenvironment for the seeded cells. In this study, a cell‐adaptable nanocolloidal hydrogel is created for 3D printing of maturable tissue constructs. The nanocolloidal hydrogel is composed of interconnected nanoparticles, which is prepared by the self‐assembly and subsequent photocrosslinking of the gelatin methacryloyl solutions. Cells can get enough space to grow and migrate within the hydrogel through squeezing the flexible nanocolloidal networks. Meanwhile, the nanostructure can promote the seeded cells to proliferate and produce matrix proteins through mechanotransduction. Using digital light process‐based 3D printing technology, it can rapidly customize cartilage tissue constructs. After implantation, these tissue constructs efficiently matured into cartilage tissues for the articular cartilage defect repair and ear cartilage reconstruction in vivo. The 3D printing of maturable tissue constructs using the nanocolloidal hydrogel shows potential for future clinical applications.

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

AbstractShell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means.Practical applicationsThis study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry.

CF-LSTM-based post-maneuver position prediction for GEO spacecraft

Prediction of the position of Geosynchronous (GEO) spacecraft after a maneuver is crucial for space domain awareness (SDA), as it can enhance the flexibility of the space surveillance network (SSN). The longitude is a unique parameter that can be freely assigned to GEO spacecraft. In this paper, we propose a prediction model for the longitude of a GEO spacecraft based on Causal Feature Long Short-Term Memory networks (CF-LSTM). Initially, we analyze the historical data of GEO spacecraft in a time-series geographic position and identify the causal parameters of longitude using the Gaussian perturbation equation. Subsequently, we present the CF-LSTM to predict the longitude. Our experimental analysis shows that the proposed method reduces the mean absolute error (MAE) and mean square error (MSE) by 60.92% and 18.75%, respectively, compared to conventional LSTM.