Scheme For Networks Research Articles

Comparative Analysis of Leak Detection Methods Using Hydraulic Modelling and Sensitivity Analysis in Rural and Urban–Rural Areas

Water scarcity is a significant global challenge, exacerbated by leakages in water distribution networks. This paper addresses the challenge of detecting leakages in rural and urban–rural water supply systems through hydraulic modelling and a sensitivity analysis. Two distinct real-world network models were studied to assess real and simulated leakage scenarios varying in location and magnitude. A distinct leakage detection approach utilizing outflow measurements from hydrants was tested. Additionally, the effectiveness of various statistical measures—such as correlation, angular closeness, Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity, and Spearman correlation—were evaluated to determine their efficacy in leakage detection. Different methods for identifying leak candidates were explored and compared, either by selecting a single leak candidate based on similarity measures or by identifying a group of candidates to mark leak hotspots. Density-based spatial clustering of applications with noise was used to assess the number of potential leak candidate groups. The study’s findings contribute to the optimization of leak detection strategies in water supply networks, particularly in rural settings, where detection is challenging due to scarce measurement datasets, budget restrictions, and operational constraints.

2D and 3D Printable Self-Healing Hydrogels Based on Polyacrylic and Tricarballylic Acids through a Double Network Strategy

2D and 3D Printable Self-Healing Hydrogels Based on Polyacrylic and Tricarballylic Acids through a Double Network Strategy

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

Traditional weed detection technology has several limitations, including low detection accuracy, substantial computational demands, and large-scale detection models. To meet the requirements of weed multi-target identification and portability, this study proposes the YOLO–WEED model for weed recognition. The proposed model has the following innovations: (1) The backbone standard convolution module in YOLOv5 was replaced by the lightweight MobileNetv3 network to simplify the network structure and reduce parameter complexity; (2) The addition of convolutional block attention module (CBAM) to the neck network enabled the model to focus on the most important features while filtering out noise and irrelevant information; (3) To further improve classification accuracy and reduce loss, the C2f module was employed to improve the C3 module in the neck network; and (4) During the model plot process, a coordinate variable was added in the box label to help the model accurately locate the weeds. In the study, six species of weeds and one crop were used as test subjects. After image enhancement techniques were used, ablation experiments were deployed. The experimental results indicated that the YOLO–WEED model achieved an average accuracy of 92.5% in identifying six types of weeds and one type of crop. The accuracies for each type of plant were 82.7%, 97.3%, 98.8%, 86%, 93.5%, 99.3% and 89.6%, respectively. The number of model parameters was reduced by 39.4% compared with YOLOv5s. Furthermore, the localisation, classification and object losses were reduced by 0.025, 0.005 and 0.014, respectively. The model optimisation and deployment of the Jetson mobile terminal for multi-target detection were realised, and the performance was better than six network models such as YOLOv5s.

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples.

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Discovery of cycloheptapeptides phakefusins A−E from the marine sponge Phakellia fusca based on molecular networking

Guided by a probe-based molecular networking strategy, five undescribed cycloheptapeptides, phakefusins A−E (1−5), were isolated from the marine sponge Phakellia fusca. Compounds 1 and 2 contain the nonproteinogenic amino acid residues of dioxindolyalanine (Dioia) and β-3-oxindolylalanine (Oia), respectively. Compound 3 possesses a unique methionine sulfoxide, whereas compound 5 includes a glutamic acid ethyl ester unit. Their structures were elucidated through NMR spectroscopy, HR-MS/MS analysis, and the advanced Marfey's method. By synthesizing the (S, S/R)-Oia standard through tryptophan oxidation, we determined the configuration of this amino acid in compound 2 using the advanced Marfey's method. These cycloheptapeptides were evaluated for their antitumor, antibacterial, and antioxidant activities. Compound 1 showed moderate cytotoxicity against MCF-7 and PC9 cells, with IC50 values of 6.8 and 9.6 μM, respectively, while compounds 2−5 demonstrated potential antioxidant effects by upregulating HO-1, NQO1, and SOD2 levels, as well as inducing Nrf2 activation.

Distributed optimization control strategy for distribution network based on the cooperation of distributed generations

AbstractAiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system.

Analyzing the impacts of cultural backgrounds on migrants' acculturation strategies in mobile phone social networks

By employing a large-scale mobile phone dataset and constructing a social network with 6,351,033 nodes and 14,735,425 ties, this research empirically studies how the individualistic and collectivistic (I-C) cultural backgrounds of migrants influence their acculturation strategies under different contexts. We introduced the rice theory and developed new network-based constructs to measure their acculturation strategies, I-C cultural backgrounds, and socioeconomic status. We found that migrants with more collectivistic cultural backgrounds tend to interact more with people from their hometowns rather than locals of their host societies. Surprisingly, this relationship is contingent on cultural distance: when a migrant's cultural background is similar to the host society, this migrant tends to interact more with local friends than people from her hometown. Moreover, higher socioeconomic status is found to decrease the cultural conservatism tendencies of migrants with more collectivistic cultural backgrounds. These findings can help stakeholders to devise more refined policies to better integrate migrants with diverse backgrounds and reduce potential cultural conflicts.

Hybrid differential evolution algorithm for Falkner-Skan flow with rotation

A hybrid differential evolution algorithm is used in this work to study the rotating transport of Falkner-Skan flow. The problem is modeled as an equivalent optimization problem by using Padé rational approximation functions. The primary model of the governing partial differential equations is imposed to subjugate the error between profiles. A hybrid evolutionary algorithm based on differential evolution and a convergent version of Nelder-Mead direct search algorithm is employed to perform global exploratory search along with an enhanced exploitation to improve the accuracy of the proposed solution scheme. The resulting scheme is named as evolutionary Padé approximation (EPA) scheme. The performance of the proposed EPA scheme on the Falkner-Skan boundary value problem is investigated by considering various values of the rotation parameters. The developed optimizer in EPA scheme was able to minimize the residuals up to10−10. Results are displayed graphically in order to study the effect of various types of parameters. EPA scheme determined that angular velocity increases or decreases accordingly as fluid parameter (β) and the rotation parameter (λ) but shows inverse behavior with respect to power law index(n). Similarly, the response of velocity profile along y−axis was decreasing function of β as well as n but increasing function of λ. The performance of the proposed EPA scheme has been demonstrated by comparing results with a hybrid neural network scheme and found in excellent agreement.

Accurate and Efficient Schemes for Mapping Out Two Isolated Seams of Conical Intersections with Neural Networks Solely Based on Adiabatic Energies: A Case Study of H+ + NO(X2Π) → H + NO+(X1Σ+).

A new scheme in the neural network (NN) diabatization approach that solely utilizes the adiabatic energies for constructing the global diabatic potential energy matrices (PEMs) of the molecular systems with two isolated seams of conical intersections (CIs) is proposed. Taking a prototype charge transfer reaction H+ + NO(X2Π) → H + NO+(X1Σ+), where two seams of CIs are located at the different linear geometries N-O-H and O-N-H, for example, the diabatization with the new scheme including a diabatic state constraint is shown to map out the topographies of both two linear CIs with 100% of the success rate in 10 different trainings, while the diabatization without such constraint hardly represents CIs, in which the avoided crossings appear instead. Simultaneously, we propose a scheme to separate the whole reactive space into three different regions and define the minimal Euclidean distances for each region to efficiently sample the energy points for the NN trainings. Through adjusting the minimal Euclidean distances, the number of the adiabatic energy data needed in the construction of diabatic PEM can be heavily decreased, lowered from ∼2000 to ∼280 energy points, which is much less than the number (>22 000) of ab initio energy points used in earlier spline diabatic PEM. Further quantum dynamic calculations show that the reaction probabilities, vibrational state distributions, and vibrational state resolved differential cross sections are well reproduced on the new NN diabatic PEM, validating these schemes for constructing the diabatic PEM.

Voltage optimal control model of regional distribution network after reconfiguration based on multi-energy conversion

In the event of a significant operational fault in the regional distribution network connected to the distributed new energy generator set, it may be necessary to quickly reconfigured certain areas of the system. This process must ensure minimal impact on the voltage of the regional distribution network. To address this issue, a voltage optimal control strategy for regional distribution network based on multi-energy conversion is proposed. Firstly, the energy conversion and adjustable characteristics of electricity, heat, and gas energy conversion equipment in the regional distribution network are analyzed in this paper. The steady-state operation model of the regional distribution network with multi-energy conversion is established. Then, the voltage optimal control model for rapid reconfiguration of the multi-energy distribution network is established by considering the two optimization objectives of power recovery for the important load and system voltage fluctuation. The model is solved based on the greedy algorithm. Finally, an equivalent model of the regional distribution network with multi-energy conversion equipment access is built based on the operation data of the distribution network in a certain area of Northeast China. The feasibility and effectiveness of the model are analyzed and verified.

Optimizing Distributions for Associated Entropic Vectors via Generative Convolutional Neural Networks.

The complete characterization of the almost-entropic region yields rate regions for network coding problems. However, this characterization is difficult and open. In this paper, we propose a novel algorithm to determine whether an arbitrary vector in the entropy space is entropic or not, by parameterizing and generating probability mass functions by neural networks. Given a target vector, the algorithm minimizes the normalized distance between the target vector and the generated entropic vector by training the neural network. The algorithm reveals the entropic nature of the target vector, and obtains the underlying distribution, accordingly. The proposed algorithm was further implemented with convolutional neural networks, which naturally fit the structure of joint probability mass functions, and accelerate the algorithm with GPUs. Empirical results demonstrate improved normalized distances and convergence performances compared with prior works. We also conducted optimizations of the Ingleton score and Ingleton violation index, where a new lower bound of the Ingleton violation index was obtained. An inner bound of the almost-entropic region with four random variables was constructed with the proposed method, presenting the current best inner bound measured by the volume ratio. The potential of a computer-aided approach to construct achievable schemes for network coding problems using the proposed method is discussed.

Preparation and Properties of Fluorinated Poly(aryl ether)s with Ultralow Water Absorption and Dielectric Constant by Cross-Linked Network Strategy.

Poly(aryl ether) materials are used in a wide range of applications in the communications and microelectronics fields for their outstanding mechanical and dielectric properties. In order to further improve the comprehensive performance, this work reports a series of cross-linkable poly(aryl ether)s (UCL-PAEn) containing trifluoroisopropyl and perfluorobiphenyl structures using 2,2-bis(4-hydroxyphenyl)hexafluoropropane, 2,2'-diallyl bisphenol A, and perfluorobiphenyl as starting materials. Their chemical structures and the effect of changes in the allyl content on the properties are thoroughly investigated. Owing to the introduction of fluorine atoms and cross-linked networks, the cross-linked poly(aryl ether) films present low dielectric constants (Dk = 1.93-2.24 at 1 MHz), low water absorption (0.14% -0.25%), and hydrophobic film surfaces (94.3-99.4°). Additionally, because of the presence of cross-linked networks, the CL-PAEn films exhibit superior thermal stability, with the 5% weight loss temperatures all above 445 °C and the maximum thermal decomposition rate temperatures all above 550 °C. The cross-linked films also demonstrate excellent mechanical properties, with tensile strength in the range of 57.1 -146.7 MPa and tensile modulus in the range of 1.8 GPa-4.5 GPa.

Bending analysis of thin plates with variable stiffness resting on elastic foundation via a two-network strategy physics-informed neural network method

A physics-informed neural network method based on a two-network strategy is introduced to address the bending problem of thin plates with variable stiffness resting on an elastic foundation. This problem is governed by a fourth-order partial differential equation (PDE), and the use of a one-network strategy to solve the PDE directly may lead to convergence issues due to singular points. Following the principles of Kirchhoff plate theory, the governing PDE is equivalently transformed into four second-order PDEs. A two-network strategy is employed for solution. We present numerical examples under various load conditions, plate geometries, foundation types, thicknesses, and material properties. The obtained results are validated against finite element method (FEM) solutions and literature. Discussions on alternative network strategies were conducted, and this study reveals that the presented two-network strategy have proven to be effective and robust. It also facilitates easier imposition of boundary conditions.

Nonlinear equalization based on feature crosses neural networks for High-speed PAM4 transmission

In this paper, a low complexity feature crosses nonlinear equalization(FC-NLE) scheme using an additional feature crosses layer based on deep neural network is proposed. A 120 Gb/s quadratic pulse amplitude modulation (PAM4) transmission system based on intensity modulation and direct detection (IM/DD) is experimentally demonstrated. After a 5 km transmission, the FC-NLE scheme achieves a bit error rate (BER) below the hard-decision forward-error-correction threshold of 7% when the received optical power exceeds 4.5 dBm. This performance surpasses that of the Volterra equalizer(VE) and network-based equalization schemes. k-means clustering and model compression techniques were adopted to effectively diminish the complexity of the FC-NLE. Experimental results show that the performance of the FC-NLE is about 0.5–1.0 dB better than that of the VE, DNN, LSTM equalizers and its complexity is less than 50% of them. Compared with other low complexity network schemes in recent years, FC-NLE still exhibits a performance improvement of more than 1 dB at lower complexity.

SusChain: a sustainable sharding scheme for UAV blockchain networks

SusChain: a sustainable sharding scheme for UAV blockchain networks

Streamlines and neural intelligent scheme for thermal transport to infinite shear rate for ternary hybrid nanofluid subject to homogeneous-heterogeneous reactions

SignificanceThe CuO, Al2O3, and TiO2 nanoparticles find extensive applications in advanced chemical reaction-based thermal transport and quadratic convective nanofluids due to their exceptional thermal properties and chemical stability. Increased thermal conductivity of these nanoparticles used to enhance heat transfer in various chemical reactors, resistance to chemical degradation and photocatalytic reactors and solar energy applications. MotiveThis study brings the investigation about quadratic convection-based thermal transport to infinite shear rate for magnetized ternary radiative cross nanofluid with homogeneous-heterogeneous chemical reactions. Water is taken as base fluid and three nanoparticles are Copper oxide (CuO), aluminium oxide (Al2O3), and titanium dioxide (TiO2). Heat transport analysis is made through quadratic convection, magnetic field and thermal radiation. Concentration of nanofluid is scrutinized though homogeneous-heterogeneous chemical reactions. Methodology: Physical problem with assumptions generates the system of partial differential equations (PDEs) and these PDEs are transformed into ordinary differential equations (ODEs) through similarity variables. Furthermore, a unique combination of Bvp4c and Levenberg Marquardt neural network (LM-NN) schemes is utilized to fetch the numerical solutions. Bvp4c is utilized to solve the governing equations, while LM-NN serves to enhance predictive capabilities and capture intricate nonlinear relationships. FindingsMagnetic environment, chemical process, radiations effects and volumetric fraction of nanoparticles make better heat transfer efficiency and control.

Computationally Efficient IMplicit Training Strategy for UNrolled NEtworks (IMUNNE): A preliminary analysis using accelerated real-time cardiac cine MRI.

Highly-undersampled, dynamic MRI reconstruction, particularly in multi-coil scenarios, is a challenging inverse problem. Unrolled networks achieve state-of-the-art performance in MRI reconstruction but suffer from long training times and extensive GPU memory cost. In this work, we propose a novel training strategy for IMplicit UNrolled NEtworks (IMUNNE) for highly-undersampled, multi-coil dynamic MRI reconstruction. It formulates the MRI reconstruction problem as an implicit fixed-point equation and leverages gradient approximation for backpropagation, enabling training of deep architectures with fixed memory cost. This study represents the first application of implicit network theory in the context of real-time cine MRI. The proposed method is evaluated using a prospectively undersampled, real-time cine dataset using radial k-space sampling, comprising balanced steady-state free precession (b-SSFP) readouts. Experiments include a hyperparameter search, head-to-head comparisons with a complex U-Net (CU-Net) and an alternating unrolled network (Alt-UN), and an analysis of robustness under noise perturbations; peak signal-to-noise ratio, structural similarity index, normalized root mean-square error, spatio-temporal entropic difference, and a blur metric were used. IMUNNE produced significantly and slightly better image quality compared to CU-Net and Alt-UN, respectively. Compared with Alt-UN, IMUNNE significantly reduced training and inference times, making it a promising approach for highly-accelerated, multi-coil real-time cine MRI reconstruction. IMUNNE strategy successfully applies unrolled networks to image reconstruction of highly-accelerated, real-time radial cine MRI. Implicit training enables rapid, high-quality, and cost-effective CMR exams by reducing training and inference times and lowering memory cost associated with advanced reconstruction methods.

Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

Incorporating hydrogen bonding networks in microporous polymer membranes for high-flux hydrocarbon separation

Polymer membranes have been most commonly utilized in gas separations, while they often suffer from the trade-off between membrane permeability and selectivity, which arises from the inherent coupling between polymer chain rigidity and interchain spacing. Here, we propose a hydrogen bonding network strategy, where hydrogen bonding acceptors are incorporated into amidoxime-functionalized polymer of intrinsic microporosity (AO-PIM) membranes to in situ construct hydrogen bonding networks with amidoxime groups, and to modulate the interchain spacing/micropore structure in AO-PIM. Especially, 2,2′-bipyridine endows AO-PIM membranes with appropriate match between polymer chain rigidity and interchain spacing, leading to dramatically enhanced C3H6 permeability of 262 Barrer, 2.9 times higher than AO-PIM membranes, with a C3H6/C3H8 selectivity of 24 under mixed-gas tests, surpassing all the reported polymer membranes and most mixed matrix membranes. We envision this scalable, generic hydrogen bonding network strategy opens an alternative avenue for polymer membranes to break the trade-off effect.