Complete Network Research Articles

False data injection attack against smart power grid based on incomplete network information

In power systems, False Data Injection (FDI) attack can covertly mislead the state estimation of the power system and lead to inestimable damage. Traditional studies on FDI attack usually assume that the attacker has complete network information of the power grid. However, this assumption is often difficult to be achieved due to the good protection of data in the control center. In this paper, an FDI attack approach with incomplete network information is proposed against AC state estimation. In addition to constructing an attack vector to modify the selected state, this approach also gives the attack range of the state. Through the analysis of the residuals we propose a method to extend the range that can evade bad data detector. Simulation in the IEEE14 and IEEE118 bus systems verify the feasibility and stealth of the model.

Engineering Detrimental Functional Groups in Conductive Additives Toward High-Performance All-Solid-State Batteries.

Conductive additives are of great importance for the adequate utilization of active materials in all-solid-state lithium batteries by establishing conductive networks in the composite cathode. However, it usually causes severe interfacial side reactions with solid electrolytes, especially sulfide electrolytes, leading to sluggish ion transportation and accelerated performance degradation. Herein, a simple hydrogen thermal reduction process is proposed on a commonly used conductive additive Super P, which effectively removes the surface oxygen functional groups and weakens the interfacial side reactions with sulfide. With a small amount of 1 wt % reduced Super P, ASSLBs demonstrates a competitive capacity of 180.2 mAh g-1 , which is much higher than the 130.8 mAh g-1 of untreated Super P. Impressively, reduced Super P based ASSLBs also exhibit a higher capacity retention of 81.8 % than 64.6 % of untreated Super P. The cathode interfacial chemical evolutions reveal that reduced Super P could effectively alleviate the side reactions of sulfide. Reduced Super P shows better reversible capacity compared to reduced carbon nanofiber with almost no loss of capacity retention, due to its more complete conductive network. Our results highlight the importance of oxygen-containing functional groups for conductive additives, lightening the prospect of low-cost 0D conductive additives for practical ASSLBs.

The emergence of circadian timekeeping in the intestine.

The circadian clock is a molecular timekeeper, present from cyanobacteria to mammals, that coordinates internal physiology with the external environment. The clock has a 24-h period however development proceeds with its own timing, raising the question of how these interact. Using the intestine of Drosophila melanogaster as a model for organ development, we track how and when the circadian clock emerges in specific cell types. We find that the circadian clock begins abruptly in the adult intestine and gradually synchronizes to the environment after intestinal development is complete. This delayed start occurs because individual cells at earlier stages lack the complete circadian clock gene network. As the intestine develops, the circadian clock is first consolidated in intestinal stem cells with changes in Ecdysone and Hnf4 signalling influencing the transcriptional activity of Clk/cyc to drive the expression of tim, Pdp1, and vri. In the mature intestine, stem cell lineage commitment transiently disrupts clock activity in differentiating progeny, mirroring early developmental clock-less transitions. Our data show that clock function and differentiation are incompatible and provide a paradigm for studying circadian clocks in development and stem cell lineages.

Preparation and thermal insulation performance of Al2O3[sbnd]Y2O3[sbnd]SiO2 ternary composite aerogels with high specific surface area and low density

Against the background of global energy shortages and the need for long-term flight safety of aerospace vehicles, traditional silica aerogels have the problems of insufficient temperature resistance and poor thermal insulation at high-temperatures, and are no longer able to meet the demand. In this study, a series of monolithic Al2O3Y2O3SiO2 ternary aerogels were successfully synthesized via synchronous sol–gel and ethanol supercritical drying. Both the prepared aerogels and the calcined samples at 1000 °C exhibited a complete three-dimensional network structure and a high specific surface area (up to 675.7 m2/g and 214.4 m2/g), which was attributed to the supporting effect of the Al2O3 component on the overall skeleton and the inhibiting effect of the Y2O3 component on the crystal transformation. Mullite fiber reinforced Al2O3Y2O3SiO2 aerogel composites were synthesized by vacuum impregnation using mullite fiber felt as a matrix. The prepared sample exhibit very low thermal conductivity (as low as 0.079 W⋅m−1·K−1 at 1000 °C). In summary, the good structural stability and high temperature thermal insulation performance make Al2O3Y2O3SiO2 ternary aerogel have the potential of efficient service in more application scenarios. This study provides a product optimization reference idea for the thermal insulation material industry.

IMDE-UGAN: Improved Memetic Direction Exploitation Optimized U-Net Generative Adversarial Network for Classification of Diabetic Retinopathy

The most important human organ is the eye which is essential for the processing and absorbing of external visual information. Diabetic retinopathy (DR) is the primary factor contributing to the global increase in blindness. To maintain eye health, people have been paying lots of attention to the problem. However, there are still lots of people who have eye problems all around the world. Thus, we proposed the Improved Memetic Direction Exploitation Optimized U-Net Generative Adversarial Network (IMDE-optimized U-Net GAN) model for DR grading. Also, for Multimodal Optimization Problems (MMOPs), a Niching Competition-based Memetic Direction Exploitation (NC-MDE) method is proposed with adaptive local search operations. Additionally, SPatially-ADaptivE (SPADE)-based conditional GANs are used to segment DR. The U-Net GAN model is used to categorize DR images. In the discriminator design, the standard classification network is modified to create the decoder-encoder network known as U-Net. The complete network error of the U-Net GAN architecture is modeled using the IMDE archives. The various performance metrics are used in the method for testing the proposed including accuracy, specificity, sensitivity, and ROC curve. As a result, the proposed method achieves 98.89% of accuracy, 98.54% for sensitivity, and specificity, it achieves 98.67%.

Psychometric network analysis of the Intuitive Eating Scale-2 in Chinese general adults.

The Intuitive Eating Scale-2 (IES-2) is a measure of intuitive eating behaviors that has been extensively validated, with traditional latent variable modeling approaches, in youth and adults from many different populations, including college students in China. However, there is still a lack of research on the psychometric properties of the IES-2 in adults from the Chinese general population. Moreover, psychometric network analysis, as a complement to traditional latent variable modeling approaches, has not been used for examining the psychometric properties of the IES-2. Thus, the present study used a psychometric network approach to evaluate the psychometric properties of the IES-2 in Chinese adults from the general population. A sample of 700 Chinese general adults (50% women; Mage = 31.13 years, SD = 9.19) recruited online were included in the present study. Psychometric network analysis was performed. Exploratory graph analysis (EGA) identified four dimensions, which were well separated in the estimated network. The network structure showed excellent stability and metric measurement invariance (i.e., network loadings) across men and women. Furthermore, several items on the IES-2 were identified as key nodes in the network of the IES-2 that may be important for the development and maintenance of intuitive eating. For example, two items (i.e., "I trust my body to tell me when to eat,"and "I trust my body to tell me when to stop eating") related to reliance on body cues were the most impactful nodes in the complete network. The findings of our study provide a greater understanding of the IES-2 from the perspective of network analysis and have implications for applications of intuitive eating interventions for general populations.

Point cloud completion network for 3D shapes with morphologically diverse structures

Point cloud completion is a challenging task that involves predicting missing parts in incomplete 3D shapes. While existing strategies have shown effectiveness on point cloud datasets with regular shapes and continuous surfaces, they struggled to manage the morphologically diverse structures commonly encountered in real-world scenarios. This research proposed a new point cloud completion method, called SegCompletion, to derive complete 3D geometries from a partial shape with different structures and discontinuous surfaces. To achieve this, morphological segmentation was introduced before point cloud completion by deep hierarchical feature learning on point sets, and thus, the complex morphological structure was segmented into regular shapes and continuous surfaces. Additionally, each instance of a point cloud that belonged to the same type of feature could also be effectively identified using HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). Furthermore, the multiscale generative network achieved sophisticated patching of missing point clouds under the same geometric feature based on feature points. To compensate for the variance in the mean distances between the centers of the patches and their closest neighbors, a simple yet effective uniform loss was utilized. A number of experiments on ShapeNet and Pheno4D datasets have shown the performance of SegCompletion on public datasets. Moreover, the contribution of SegCompletion to our dataset (Cotton3D) was discussed. The experimental results demonstrated that SegCompletion performed better than existing methods reported in the literature.

Effect of Needle Gauge Size on Pain During Intravitreal Anti-VEGF Injection: A Systematic Review and Network Meta-Analysis.

Concerns related to pain from intravitreal injections are one of the key factors mentioned by patients when asked about therapy. In this systematic review and network meta-analysis, we evaluate the literature of comparative clinical trials on the relationship between needle gauge size and pain experience during intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy. We searched 12 literature databases on 14 October 2023 for comparative studies of gauge sizes for intravitreal anti-VEGF injections. The primary outcome of interest was the reported pain experience immediately after the injection. All outcomes of pain were transformed into standardized effect sizes using Cohen's d. Using a network meta-analysis approach, we were able to compare all gauge sizes and rank them according to the reported pain experience. We identified nine eligible studies with data on a total of 998 patients and 1004 eyes. Needle sizes studied were 26-gauge, 27-gauge, 29-gauge, 30-gauge, 32-gauge, 33-gauge, and 34-gauge. A complete network was present, which allowed for a network meta-analysis. We used the thickest (26-gauge) needle as the reference group and observed a clear trend of lower pain experience with thinner gauge sizes (d: -0.4, d: -2.7, d: -3.8, d: -4.8, d: -4.5, and d: -5.3; respectively, for 27-gauge, 29-gauge, 30-gauge, 32-gauge, 33-gauge, and 34-gauge). A gauge size of 30 or thinner may minimize patient discomfort related to intravitreal anti-VEGF therapy.

Characterization of network harmonic impedance for grid connection studies of renewable plants

The impedance of a grid as a function of the frequency, or grid harmonic impedance, indicates the nature of the distortion and, especially, of the resonances that may appear in the system due to the presence of harmonic sources. Modelling this impedance is fundamental to be able to perform grid connection studies of renewable generation plants. However, usually the necessary information is not available, and the impedance is represented in a very simplified way. This paper analyses the different methods proposed in the literature to model the grid harmonic impedance and selects the most suitable for its implementation in PowerFactory simulation software, widely used in the industry for this type of studies. The selected method allows obtaining an equivalent network model that reproduces the harmonic behaviour of the network, without having to model the complete network in detail. A case study that applies the method to analyse the harmonic distortion at the POI of an offshore wind farm is presented.

A coarse-to-fine point completion network with details compensation and structure enhancement

Point cloud completion, the issue of estimating the complete geometry of objects from partially-scanned point cloud data, becomes a fundamental task in many 3d vision and robotics applications. To address the limitations on inadequate prediction of shape details for traditional methods, a novel coarse-to-fine point completion network (DCSE-PCN) is introduced in this work using the modules of local details compensation and shape structure enhancement for effective geometric learning. The coarse completion stage of our network consists of two branches—a shape structure recovery branch and a local details compensation branch, which can recover the overall shape of the underlying model and the shape details of incomplete point cloud through feature learning and hierarchical feature fusion. The fine completion stage of our network employs the structure enhancement module to reinforce the correlated shape structures of the coarse repaired shape (such as regular arrangement or symmetry), thus obtaining the completed geometric shape with finer-grained details. Extensive experimental results on ShapeNet dataset and ModelNet dataset validate the effectiveness of our completion network, which can recover the shape details of the underlying point cloud whilst maintaining its overall shape. Compared to the existing methods, our coarse-to-fine completion network has shown its competitive performance on both chamfer distance (CD) and earth mover distance (EMD) errors. Such as, the repairing results on the ShapeNet dataset of our completion network are reduced by an average of 35.62%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$35.62\\%$$\\end{document}, 33.31%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$33.31\\%$$\\end{document}, 29.62%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$29.62\\%$$\\end{document}, and 23.62%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$23.62\\%$$\\end{document} in terms of CD error, comparing with PCN, FoldingNet, Atlas, and CRN methods, respectively; and also reduced by an average of 15.63%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15.63\\%$$\\end{document}, 1.29%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.29\\%$$\\end{document}, 64.52%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$64.52\\%$$\\end{document}, and 62.87%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$62.87\\%$$\\end{document} in terms of EMD error, respectively. Meanwhile, the completion results on the ModelNet dataset of our network have an average reduction of 28.41%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$28.41\\%$$\\end{document}, 26.57%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$26.57\\%$$\\end{document}, 20.65%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20.65\\%$$\\end{document}, and 18.55%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$18.55\\%$$\\end{document} in terms of CD error, comparing to PCN, FoldingNet, Atlas, and CRN methods, respectively; and also an average reduction of 21.91%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$21.91\\%$$\\end{document}, 19.59%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$19.59\\%$$\\end{document}, 43.51%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$43.51\\%$$\\end{document}, and 21.49%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$21.49\\%$$\\end{document} in terms of EMD error, respectively. Our proposed point completion network is also robust to different degrees of data incompleteness and model noise.

High-temperature creep behaviour and deformation mechanism of a high-concentration Re/Ru single-crystal nickel-based alloy

In this study, the deformation and damage mechanisms of a high-concentration Re/Ru single-crystal nickel-based alloy at ultrahigh temperatures are studied via creep performance tests, microstructural observations and dislocation configuration diffraction contrast analyses at 1170 °C. The results show that the alloy has good mechanical properties. In addition, the creep life is 155 h at 1170 °C and 120 MPa. During creep, dislocations moving in the γ matrix can react with each other to form a dense and complete dislocation network. This dense and complete dislocation network can effectively prevent dislocations from shearing into the γ′ phase and reduce the creep rate during the steady-state creep period. In the late stage of ultrahigh-temperature creep, stacking faults are found in the γ′ phase, indicating that the alloy has a low stacking fault energy and that the stacking faults that form in this alloy can serve as obstacles for dislocation movement. In addition, the dislocations that cut into the γ′ phase in the late stage of ultrahigh temperature creep can cross-slip from the {111} plane to the {100} plane, forming a KW lock, which can inhibit the slip and cross-slip dislocation movement modes. Therefore, the presence of dislocation networks, stacking faults and KW locks in alloys is the main reason for the relatively low strain rate and good creep resistance of these alloys under ultrahigh-temperature creep conditions. In the late stage of creep, the initiated dislocations slip in two orientations, resulting in kinking of the rafted γ′ phase and crack initiation at the top interface of the twisted rafted γ/γ′ two phases. Under the action of shear stress, the crack generates tensile stress perpendicular to the stress axis, and it expands in this direction. This crack is the damage mechanism of the alloy during ultrahigh-temperature creep.

Comparing different methods for connecting bike lanes to generate a complete bike network and identify potential complete streets in Atlanta

This study compares two different strategies for connecting bike networks – traditional design-based and algorithm-supported – to investigate how their results differ along metrics such as proportion of bike lanes along simulated routes and the resulting cycling stress. The objective is to find optimal strategies for connecting isolated existing cycling infrastructure to form complete networks that improve both active mobility and public transit ridership. By aligning the bike network with transit and activity locations, this research develops an algorithmic framework for generating a skeleton of multimodal networks best suited to become "complete streets." The network generated through an algorithm is compared with a proposed traditionally designed network to determine their relative network performance. The findings suggest that a judicious combination of traditionally designed, and algorithm-supported networks offer better cycling infrastructure than either strategy alone. In addition, algorithms can also be developed to indicate the potential for street segments to be complete streets.

Stable switching behavior of low-temperature ZrO2 RRAM devices realized by combustion synthesis-assisted photopatterning

We have realized efficient photopatterning and high-quality ZrO2 films through combustion synthesis and manufactured resistive random access memory (RRAM) devices with excellent switching stability at low temperatures (250 °C) using these approaches. Combustion synthesis reduces the energy required for oxide conversion, thus accelerating the decomposition of organic ligands in the UV-exposed area, and promoting the formation of metal-oxygen networks, contributing to patterning. Thermal analysis confirmed a reduction in the conversion temperature of combustion precursors, and the prepared combustion ZrO2 films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice, along with an amorphous phase. Furthermore, the synergistic effect of combustion synthesis and UV/O3-assisted photochemical activation resulted in patterned ZrO2 films forming even more complete metal-oxygen networks. RRAM devices fabricated with patterned ZrO2 films using combustion synthesis exhibited excellent switching characteristics, including a narrow resistance distribution, endurance of 103 cycles, and retention for 105 s at 85 °C, despite low-temperature annealing. Combustion synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.

Spatial-Temporal Dynamics of Urban Green Spaces in Response to Rapid Urbanization and Urban Expansion in Tunis between 2000 and 2020

Over the past two decades, the rate of urbanization has increased significantly worldwide, with more than half of the population already living in cities; this trend continues in numerous countries and regions. Tunisia is a North African country with a rich history and diverse cultural heritage. In Greater Tunis, its capital city, urbanization has accelerated since 1960. Rapid urbanization has increased the demand for grey infrastructure and led to changes in land-use patterns and the destruction of the environment. This study aims to understand and depict the relationship between urban expansion and the green infrastructure in the Greater Tunis area. This study uses land-use data, administrative boundaries vector data, and Google satellite imagery datasets to calculate and analyze the changes in the land use transfer matrix and landscape pattern index of built-up land and green spaces in the Tunisian capital for three periods: 2000, 2010, and 2020. We found that the expansion of built-up areas in Tunis has increased from 8.8% in 2000 to 12.1% in 2020, and changes in green spaces have decreased from 23% in 2000 to 20.9% in 2020. Without planning guidelines, the layout of green spaces has become more fragmented and disorganized. For this reason, we provide programs and suggestions for building a complete ecological network of green spaces in order to provide references and lessons for related studies and cities facing the same problems.

A Heterogeneous Inference Framework for a Deep Neural Network

Artificial intelligence (AI) is one of the most promising technologies based on machine learning algorithms. In this paper, we propose a workflow for the implementation of deep neural networks. This workflow attempts to combine the flexibility of high-level compilers (HLS)-based networks with the architectural control features of hardware description languages (HDL)-based flows. The architecture consists of a convolutional neural network, SqueezeNet v1.1, and a hard processor system (HPS) that coexists with acceleration hardware to be designed. This methodology allows us to compare solutions based solely on software (PyTorch 1.13.1) and propose heterogeneous inference solutions, taking advantage of the best options within the software and hardware flow. The proposed workflow is implemented on a low-cost field programmable gate array system-on-chip (FPGA SOC) platform, specifically the DE10-Nano development board. We have provided systolic architectural solutions written in OpenCL that are highly flexible and easily tunable to take full advantage of the resources of programmable devices and achieve superior energy efficiencies working with a 32-bit floating point. From a verification point of view, the proposed method is effective, since the reference models in all tests, both for the individual layers and the complete network, have been readily available using packages well known in the development, training, and inference of deep networks.

The improved thermal conductivity and heat dissipation capacity of elastomer‐based thermal interface materials through promoting the surface interactions and complete networks

AbstractPolymer‐based thermal interface materials (TIMs) with excellent thermal conductivity and heat dissipation capabilities play a crucial role in addressing the issue of heat accumulation in advanced integrated electronics. However, establishing improved surface interactions and complete networks to enhance efficient phonon transfer remains a significant challenge. To tackle this problem, surface modification and ice‐templating techniques are commonly employed to create the robust interface crosslinks and continuously thermal conductive pathways. Herein, 2‐mercaptobenzimidazole (MB) was used as a reducing and modifying agent to functionalize graphene oxide (rGO‐MB) within the three‐dimensional networks, which was prepared using a combination of hydrothermal and ice‐templating methods. As a result, the reduced graphene oxide/natural rubber (rGO‐MB/NR) TIMs exhibited a remarkable through‐plane thermal conductivity of 0.93 W m−1 K−1 with a filler loading of 3 wt%. The enhanced interface interactions between rGO‐MB and NR, combined with the establishment of a three‐dimensional network, significantly contributed to the improved thermal conductivity and heat dissipation capabilities. Moreover, the obtained TIMs demonstrated favorable mechanical properties (5.06 MPa, 502%) and excellent insulation performance (3 × 1013 Ω cm). These findings provide the valuable insights into potential solutions for mitigating heat accumulation issues in next‐generation electronics.Highlights rGO‐MB with thiol groups was obtained by the hydrothermal method. Interactions between rGO‐MB and NR mainly depended on the chemical bonds. rGO‐MB/NR TIMs owned the improved thermal management performance.

Charging management of electric vehicles with the presence of renewable resources

Considering the increasing use of electric vehicles, the establishment of charging stations to exchange power between the grid and electric devices, and the integration of charging stations with solar power generation sources, the optimal use of electric vehicle charging stations in the power system. The purpose of cost reduction in the presence of the intelligent environment is a challenge that must be investigated so that this platform is suitable for predicting the behaviour of vehicles and, as a result, optimizing their presence in the power network. This research presents a relatively complete radial distribution network development planning model in two scenarios. In the first scenario, the effects of electric vehicles are not considered, and only the effects of distributed production (renewable and dispatchable) are considered. Studies have been done on a sample 54-bus network, a common system in most Distribution expansion planning (DEP) articles for distribution networks. In addition, the real data of American highways have been used to create raw input data. Also, due to the distance limit, the information on vehicles under 100 miles has been received as electric vehicle information. The clustering method and Capiola multivariate probability distribution functions have created suitable vehicle scenarios during different planning years. Capiola’s method increases the accuracy of vehicle load forecasting according to a predetermined growth rate. The DEP problem in this research is modeled as an optimization problem based on scenario, dynamic, and in 5 one-year time frames (5-year time horizon and one-year accuracy). The results indicate that, in the presence of electric vehicles and distributed production sources, the technical characteristics of the network are improved.Similarly, the use of DGs, in addition to reducing the cost of equipment, has reduced undistributed energy in the system. But 10,000 vehicles, which have been applied to the network as an uncontrolled load, have caused an increase in undistributed energy. The cost of equipment required for the network development is almost as much as 5%.

LEOCN: Real-time and complete network simulation framework for LEO constellation networks1

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.

Strategies of state penetration: a network analysis of community governance in Shanghai

IntroductionThe Chinese state has been found to penetrate community participation to strengthen state infrastructure power, but understanding of these strategies remains equivocal.MethodsThis paper collect complete network data from a sample of 112 residents who were active in community activities in Kang community in Shanghai, China. Kang is an award-winning community for its active participation and excellent governance. We examine the strategies of state penetration on the relation between residents' committee (RC) and the thick network through routine community activities.ResultsThe network is indeed horizontal (rather than hierarchical) around the RC. Instead of manifesting state power, popularity within the network translates to decision-making power in deciding routine community activities. However, residents with high network popularity are affiliated with the state, and this association may be generated by the state itself through a deliberate process of cultivation and co-optation.DiscussionThese findings shed light on the nuanced strategies of state penetration. Rather than overt suppression or infiltration, the state exerts authority over a horizontal network, which ensures that self-organized community participation occurs under state domination.

Temperature dependence of mechanical strength in HPDC Mg–6Y–3Zn–1Al alloy with LPSO phase

The temperature dependence of mechanical strength including yield strength (YS) and ultimate tensile strength (UTS) in HPDC WZA631 alloy is investigated in a wide temperature range from room temperature (RT) to 350 °C. It is found that at 25–300 °C, YS and UTS do not drop markedly, from 173 MPa and 274 MPa at RT to 113 MPa and 170 MPa at 300 °C, respectively. While above 300 °C the flow stress falls rather rapidly, to 74 MPa for YS and to 108 MPa UTS at 350 °C. The (Al,Zn)2Y phase exhibits similar behavior between 150 °C and 350 °C, breaking up under stress and becoming the relatively stable crack source without catastrophic cracking. The LPSO phase, keeping steady and excellent critical resolved shear stress (CRSS) below 250 °C, remains unchanged with complete network structure at 150 °C and 250 °C. Therefore, the LPSO phase reinforces the alloy with slow flow stress reduction at RT-300 °C for WZA631 alloy. The tensile property deteriorates above 300 °C due to the destruction of the network structure as well as the reduction of CRSS for the prismatic slip of LPSO under tensile loading. Thus, the LPSO phase determines the mechanical strength evolution of WZA631 alloy, which is similar to the behavior of γ’ phase in the Ni-based superalloys. Besides, the relationship between strength and temperature is described by the Arrhenius equation, which may be reduced to an exponential model in the form of σ=a+bT for HPDC WZA631 alloy.