Flexible Network Research Articles

A single-particle energy-conserving dissipative particle dynamics approach for simulating thermophoresis of nanoparticles in polymer networks.

The transport of nanoparticles in polymer networks has critical implications in biology and medicine, especially through thermophoresis in response to temperature gradients. This study presents a single-particle energy-conserving dissipative particle dynamics (seDPD) method by integrating a single-particle model into the energy-conserving DPD model to simulate the mesoscopic thermophoretic behavior of nanoparticles in polymer matrices. We first validate the newly developed seDPD model through comparisons with analytical solutions for nanoparticle viscosity, thermal diffusivity, and hydrodynamic drag and then demonstrate the effectiveness of the seDPD model in capturing thermophoretic forces induced by temperature gradients. The results show that nanoparticles driven by the Soret forces exhibit unique transport characteristics, such as drift velocity and diffusivity, leading to a significant acceleration of nanoparticle diffusion in the polymer network, which has been known as the giant acceleration of diffusion. Quantifying how nanoparticles move in flexible polymer networks sheds light on the interaction dynamics of nanoparticles within polymer networks, providing insight into nanoparticle behavior in complex environments that could be leveraged in various applications from drug delivery to material design.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

Designing from biobased to closed-loop circularity: Flexible dynamic polyimine-amide networks

Designing from biobased to closed-loop circularity: Flexible dynamic polyimine-amide networks

Polymer nanocomposites based on flexible BaTiO3 networks anchored with gold nanoparticles towards simultaneous high dielectric permittivity and low loss

Polymer composites filled with conductive fillers have been proven to have ultra-high effective permittivity near percolation, but they usually have high dielectric loss due to the easy contact between the conductive fillers. Herein, a polymer composite with a three-dimensional BaTiO3 ceramic skeleton loaded with gold nanoparticles, which effectively avoids the direct contact of gold nanoparticles, is proposed. Benefiting from numerous equivalent micro-capacitors and the Coulomb blockade effect of gold nanoparticles, obviously enhanced permittivity and low loss are concurrently achieved. Particularly, with a gold sputtering time of 12 min, a high permittivity of 47.7@10 kHz, which is approximately 454 % (i.e. from 8.2 to 47.7@10 kHz) of the PVDF matrix, is achieved in the composite. Meanwhile, a low loss of about 0.032, which is merely about 65 % that of pure PVDF (0.0492@10 kHz) is maintained. This work provides an effective approach for designing polymer composites with high permittivity and low loss.

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.

Routing Algorithms for SDM Flexible Optical Networks

This paper considers the online routing, modulation, spectrum, and core allocation problem in elastic optical networks. Three algorithms are proposed to minimize the bandwidth blocking probability while taking into account the impact of interference from established lightpaths. These algorithms are based on a sequence of alternative paths, ordered in terms of increasing lengths, determined by Yen’s algorithm. Each of the algorithms for the selected single or dual lightpath enforces the constraints of spectrum continuity, contiguity of the slots, and non-overlapping of the spectra with the established lightpaths, maximizes the number of bits per symbol, and selects a specific core on each link of the path. Simulation studies performed for two sample networks showed that the crosstalk has a significant impact on the bandwidth blocking probability. The best results were obtained by iterating over all paths and all slots, selecting the lightpath with the lowest average crosstalk per slot, consisting of the most loaded cores.

Design and Application of Real‐Time and High‐Precision Seismic Acquisition Station in Antarctic Region

ABSTRACTIn this study, a polar seismic acquisition station based on the FPGA + ARM architecture was successfully developed. This system can achieve real‐time multifunctional acquisition and transmission of seismic signals and environmental information in the extreme low‐temperature environment of Antarctica at −40°C. The system adopts a modular design, with high‐precision data acquisition encoded by the main controller and uploaded to the host computer in real‐time. Integrated display controls enable real‐time data display and acquisition station location mapping for user interaction. We propose improving data transmission in traditional seismic acquisition stations by adopting a distributed computing model with data segment return. Integrating the first arrival picking algorithm as an IP core will reduce computational demands on the data processing center. This acquisition system features high precision (24‐bit), a wide dynamic range (> 130dB), low noise (0.5 V rms@40dB), and high‐precision timing control (200 ns). It supports real‐time wireless data transmission over 120 channels up to 1 km. The system also implements a self‐storage plus wired and wireless networking solution, allowing flexible networking based on actual needs. Test results indicate that the ice sheet thickness at the acquisition site is approximately 526.49 m, validating the experimental design's effectiveness and providing technical support for subsequent drilling and seismic exploration.

Hierarchical Co(OH)2 nanosheets-attached flexible non-woven fabric network coupled with iron oxides nanoparticles-deposited nitrogen-doped carbon for supercapacitor application

Hierarchical Co(OH)2 nanosheets-attached flexible non-woven fabric network coupled with iron oxides nanoparticles-deposited nitrogen-doped carbon for supercapacitor application

Intelligent parking lot power management: Augmented epsilon‐constraint concept with correlation analysis

AbstractThis paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants’ output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method.

Microsoft applications of 6G in V2X

Abstract. With the rapid advancement of intelligent transportation systems and autonomous driving technologies, 6G networks, as a key technology for future communications, are set to bring trans-formative changes to V2X (Vehicle-to-Everything) communication. This paper explores the application prospects of 6G in V2X, with a focus on the critical technological support 6G provides to vehicular networks. The high transmission rates and ultra-low latency of 6G will significantly enhance real-time interactions between vehicles and between vehicles and infrastructure, improving traffic safety and efficiency. Additionally, 6G's broad connectivity and flexible network architecture will support a greater number of simultaneous device connections, facilitating the widespread adoption of intelligent transportation systems. However, 6G faces challenges in V2X applications, including security issues, privacy protection, and internet power supply concerns. The paper analyzes these challenges, proposes corresponding solutions, and envisions the future development of 6G technology in the V2X domain.

Multi-Agent Deep Reinforcement Learning-Based Distributed Voltage Control of Flexible Distribution Networks with Soft Open Points

The increasing number of distributed generators (DGs) leads to the frequent occurrence of voltage violations in distribution networks. The soft open point (SOP) can adjust the transmission power between feeders, leading to the evolution of traditional distribution networks into flexible distribution networks (FDN). The problem of voltage violations can be effectively tackled with the flexible control of SOPs. However, the centralized control method for SOP may make it difficult to achieve real-time control due to the limitations of communication. In this paper, a distributed voltage control method is proposed for FDN with SOPs based on the multi-agent deep reinforcement learning (MADRL) method. Firstly, a distributed voltage control framework is proposed, in which the updating algorithm of the intelligent agent of MADRL is expounded considering experience sharing. Then, a Markov decision process for multi-area SOP coordinated voltage control is proposed, where the control areas are divided based on electrical distance. Finally, an IEEE 33-node test system and a practical system in Taiwan are used to verify the effectiveness of the proposed method. It shows that the proposed multi-area SOP coordinated control method can achieve real-time control while ensuring a better control effect.

Flexibility-Constrained Energy Storage System Placement for Flexible Interconnected Distribution Networks

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large.

Zn(II)-based mechanically flexible metallosupramolecular network: Investigating rheology, morphology, anti-bacterial effect and semiconducting device performances

Synthesis of supramolecular Zn(II)-metallogel of succinic acid with N,N′-dimethyl formamide (DMF) solvent has been explored in this present study. Succinic acid is used as a low molecular weight gelator (LMWG) and DMF acts as a metallogel-immobilized solvent-media. Mechanical flexibility, viscous, and elastic behaviours of the metallogel are characterized by exhaustive rheological experiments. Different rheological parameters like storage modulus (G′) and loss modulus (G″) are evaluated for certain regions of shear strain and angular frequency. The morphological decoration of xerogel material obtained from Zn(II)-based metallosupramolecular gel (i.e., Zn-Succinic) has been visualized through field emission scanning electron microscope (FESEM). Energy dispersive X-ray analysis (EDX) verifies the active involvement of metallogel-forming chemical constituents. The flexible metallosupramolecular scaffold-construction strategy has been unveiled through infrared spectroscopic data and ESI-Mass investigations. The antibacterial potency of Zn(II)-directed metallogel has also been verified against several Gram-positive bacteria (i.e., Bacillus cereus (ATCC 13061), Bacillus subtilis (MTCC 121), Listeria monocytogenes (MTCC 657), Staphylococcus aureus (MTCC 96)), and two Gram-negative bacterial strains (i.e., Salmonella typhimurium (MTCC 98) and Escherichia coli (MTCC 1667)). Even, Zn-Succinic metallogel exhibits Schottky diode behaviour with a significant non-linear rectifying nature in I-V curve, proving the semiconducting diode features with electrical parameters.

Kill Two Birds with One Stone: Cracking Lithography Technology for High-Performance Flexible Metallic Network Transparent Conductors and Metallic Micronano Sheets.

Flexible electronics have sparked a wide range of exciting applications, such as flexible display technologies, lighting, sensing, etc. Flexible conductors are essential components of flexible electronics and seriously affect efficiency and overall performance. Here, a facile and kill-two-birds-with-one-stone strategy of cracking lithography technology has been proposed to simultaneously fabricate two high-performance flexible conductors, flexible metallic network transparent conductors (f-NTCs) and metallic micronano sheets (MMNSs). The PET substrate flexible transparent conductors (FTCs) based on this strategy yield 88.1% transparency within the visible spectrum and a sheet resistance of 9 Ω/sq. In addition, the FTCs show exceptional mechanical stability, with the sheet resistance remaining virtually unchanged even after 6000 s of bending tests. Subsequently, the remaining MMNSs are recycled to manufacture a Ag paste, showing a very low conductive percolation threshold (∼13%) and excellent flexibility with 140% breaking elongation. After 1000 s of stretching tests, it showed excellent mechanical stability. Furthermore, flexible electroluminescent devices based on the FTCs and sensors fabricated with MMNSs both show excellent performance, demonstrating their potential wide applications in flexible electronics.

Forecasting Electricity Consumption Using Function Fitting Artificial Neural Networks and Regression Methods

With the growth of smart grids, consumers now have access to new technologies that enable improvements in the quality of service provided and allow new levels of energy efficiency. Much of this increase in energy efficiency is directly related to changes in consumption habits due to the quantity and quality of information made available by new technologies. At this point, short-term consumption forecasting can be considered an effective information tool in the search for better consumption patterns and energy efficiency. This paper presents prediction tests combining the result obtained from an artificial neural network and regression methods. The artificial neural network used was the Multilayer Perceptron (MLP), and its results were compared with polynomial regression techniques (first, second, and third degree), demonstrating the superiority of the network. The neural network has proven to be a highly effective tool for forecasting future data, demonstrating its ability to capture complex patterns in input data and produce accurate estimates. Additionally, the flexibility of neural networks in handling large volumes of data and their continuous adjustment capability further enhance their suitability as a robust tool for future predictions. The results corroborate the capacity of the methodology presented for short-term consumption forecasting.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

Flexible and Efficient Network Slicing for Integrated Optical Metro Networks With Diverse Access Applications

Flexible and Efficient Network Slicing for Integrated Optical Metro Networks With Diverse Access Applications

Time-Domain Fault Detection and Location Scheme for Flexible DC Distribution Networks

Accurately detecting and locating the fault point of the DC line is significant for eliminating the fault and restoring the power supply of the flexible DC distribution network as soon as possible. Firstly, a direction pilot protection scheme for a complex DC distribution network is proposed based on the integral of the current superposition to identify the fault direction. Then, an online time-domain fault location method based on the least square (LS) method to solve the overdetermined equations is proposed by analyzing the fault loop circuit after the DC line fault. The proposed location scheme utilizes fault data at both ends of the line to eliminate the theoretical impact of fault resistance, and the calculation of the current difference method is discussed to reduce the location error of whether the fault current-limiting reactor (CLR) exists. Finally, various simulations by PSCAD/EMTDC V4.5 demonstrate that the proposed scheme has high protection reliability and location results after different fault positions and resistances. The proposed scheme has low requirements for the sampling rate, fault data length, and implementation costs, which can meet practical application requirements.

Retraction Note: Network traffic reduction with spatially flexible optical networks using machine learning techniques

Retraction Note: Network traffic reduction with spatially flexible optical networks using machine learning techniques

A 3D flexible conductive network skeleton by SWCNT-COOH and PVA-PAA enabling high performance for Si anode

Silicon anode undergoes dramatic volume expansion during charge and discharge, causing structural and interfacial instability that severely degrades battery cycling performance. Herein, we propose a three-dimensional (3D) flexible self-standing SiMP/SWCNT-COOH@PVA/PAA anode, in which carboxylation-modified single-walled carbon nanotubes (SWCNT-COOH) construct a highly conductive and flexible skeleton to provide rapid electron transport channels for micron silicon (SiMP), while polyacrylic acid-polyvinyl alcohol (PVA-PAA) cross-linking layer forms a high-strength polymer interface to strengthen the conductive skeleton and helps to maintain the structural integrity of SiMP. The resulting electrode exhibits ultra-high electrical conductance (12406 S m−1) and exceptional mechanical characteristic (tensile strength of 46.95 MPa). More importantly, it demonstrates extremely stable electrochemical properties, with an impressive maximum capacity of approximately 2868.5 mAh g−1 at 0.2C, while retaining an exceptional 96.49 % of capacity even after 100 cycles. A consistent capacity of 1000 mAh g−1 at 1C throughout 1000 cycles is also realized. Furthermore, a flexible full battery based on SiMP/SWCNT-COOH@PVA/PAA anode achieves a discharge capacity of 49.5 mAh g−1 following 100 cycles, and illuminates a LED strip normally under various bending conditions. These results are expected to open a new way towards enhanced performance silicon-based electrodes within flexible LIBs.