Search

The diffusion of small molecular penetrants through polymeric materials represents an important fundamental problem, relevant to the design of materials for applications such as coatings and membranes. Polymer networks hold promise in these applications because dramatic differences in molecular diffusion can result from subtle changes in the network structure. In this paper, we use molecular simulation to understand the role that cross-linked network polymers have in governing the molecular motion of penetrants. By considering the local, activated alpha relaxation time of the penetrant and its long-time diffusive dynamics, we can determine the relative importance of activated glassy dynamics on penetrants at the segmental scale vs entropic mesh confinement on penetrant diffusion. We vary several parameters, such as the cross-linking density, temperature, and penetrant size, to show that cross-links primarily affect molecular diffusion through the modification of the matrix glass transition, with local penetrant hopping at least partially coupled to the segmental relaxation of the polymer network. This coupling is very sensitive to the local activated segmental dynamics of the surrounding matrix, and we also show that penetrant transport is affected by dynamic heterogeneity at low temperatures. To contrast, only at high temperatures and for large penetrants or when the dynamic heterogeneity effect is weak, does the effect of mesh confinement become significant, even though penetrant diffusion more broadly empirically follows similar trends as established models of mesh confinement-based transport. [ABSTRACT FROM AUTHOR]

We generalize a microscopic statistical mechanical theory of the activated dynamics of dilute spherical penetrants in glass-forming liquids to study the influence of crosslinking in polymer networks on the penetrant relaxation time and diffusivity over a wide range of temperature and crosslink fraction (fn). Our calculations are relevant to recent experimental studies of a nm-sized molecule diffusing in poly-(n-butyl methacrylate) networks. The theory predicts the penetrant relaxation time increases exponentially with the glass transition temperature, Tg(fn), which grows roughly linearly with the square root of fn due to the coupling of local hopping to longer-range collective elasticity. Moreover, Tg is also found to be proportional to a geometric confinement parameter defined as the ratio of the penetrant diameter to the mean network mesh size. The decoupling ratio of the penetrant and Kuhn segment alpha times displays a complex non-monotonic dependence on fn and temperature that is well collapsed based on the variable Tg(fn)/T. A model for the penetrant diffusion constant that combines activated relaxation and entropic mesh confinement is proposed, which results in a significantly stronger suppression of mass transport with degree of effective supercooling than predicted for the penetrant alpha time. This behavior corresponds to a new network-based type of "decoupling" of diffusion and relaxation. In contrast to the diffusion of larger nanoparticles in high temperature rubbery networks, our analysis in the supercooled regime suggests that for the penetrants studied the mesh confinement effects are of secondary importance relative to the consequences of crosslink-induced slowing down of activated hopping of glassy physics origin. [ABSTRACT FROM AUTHOR]

Wireless sensors networks (WSN) become more popular in recent years. For their efficient use, it is necessary to define energy-efficient routing protocols. In WSN, it sometimes arrives that some sensors have items that belong or not to them. The aim being to send each item to its real destination. This problem is known as the permutation routing problem. Some sensors may fail during the routing process. These particular cases must not prevent the operation of routing as a whole. Thus, it becomes important to ensure the fault-tolerance of the routing mechanism. In this paper we propose an energy-efficient and fault-tolerant permutation routing protocol for WSN. The proposed PEFTOSPRO protocol is realized in four stages. Firstly, we partition the sensors in cliques within which Cluster Heads (CH) are elected. Secondly, we achieve the hierarchical clustering of CH. Thirdly, we route items to their belonging cliques. And finally, we route each item to it real destination within all the cliques. We use the wake and sleep technique and the fault-tolerance in the data routing process in order to save the sensors' energy. Simulations show that the wake and sleep technique and the fault tolerance management during the data routing process help to save the sensors' energy, and by doing so, prolong the lifetime of the WSN. PEFTOSPRO is a suited protocol to solve the permutation routing problem in a multi-hop environment for wireless sensor networks, because it ensures that sensors efficiently use their energy and takes into account the fault tolerance. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to investigate and evaluate the performance of wireless Ad-Hoc routing protocols using the OPNET simulation tool, as well as to recommend the most effective routing strategies for the wireless mesh environment. Investigations have been testified to analyze the performance of the reactive and proactive Ad-Hoc routing protocols in different scenarios. Application and wireless metrics were configured that were used to test and evaluate the performance of routing protocols. The application metric includes web browsing metrics such as HTTP page response time, voice and video metrics such as end-to-end delay, and delay variation. The wireless network metrics include wireless media access delay, data dropped, wireless load, wireless retransmission attempts, and Packet Delivery Ratio. The simulations results show that the AODV overcome DSR and OLSR in terms of PDR (76%), wireless load (22.692 Mbps), voice delay variation (102.685 ms), HTTP page response time (15.317 sec), voice and video packet end-to-end delay (206.527 and 25.294 ms), wireless media access delay (90.150 ms), data dropped (10.003 Mbps), wireless load (22.692 Mbps), and wireless retransmission attempts (0.392 packets). [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio networks (CRNs) have emerged as a promising solution to address the spectrum scarcity problem by allowing unlicensed secondary users (SUs) to opportunistically access the underutilized licensed spectrum bands. However, efficient channel assignment in CRNs, especially with user mobility and power control considerations, remains a challenging problem. Traditional channel assignment algorithms often struggle to adapt to changing network conditions, resulting in suboptimal performance. To address these challenges, this research presents a novel Taylor Tuna Optimizer‐based hybrid deep Q‐network (TayTO‐based HDQNet) for channel assignment in CRNs with user mobility and power control considerations. The proposed TayTO‐based HDQNet combines the Elman neural network architecture with the reinforcement learning framework of deep Q‐networks, tuned using the Taylor Tuna Optimizer. This hybrid approach enables intelligent channel assignment decisions while considering power consumption constraints, leading to efficient communication in CRNs. To train the HDQNet agent, a comprehensive dataset is generated through simulations of various CRN scenarios, incorporating channel availability, interference levels, and power consumption for different channel assignment decisions. The HDQNet agent undergoes iterative training using this dataset to develop an optimal channel assignment policy. Experimental results demonstrate the effectiveness of the proposed TayTO‐based HDQNet approach done by MATLAB, achieving a high success rate of 98.47% and surpassing the reliability scores of previous studies. This highlights the improved performance and reliability of the proposed approach for channel assignment in CRNs. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ε‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN. [ABSTRACT FROM AUTHOR]

Given a graph G and an edge $ e\in E(G) $ e ∈ E (G) , let S be a vertex set of G. For any two vertices x, $ y\in S $ y ∈ S , if e belongs to all the shortest paths between x and y, then x and y can monitor the edge e. For each edge e of G, if there exists x and y in S such that x and y can monitor e, then the set S can be called a monitoring-edge-geodetic ( $ \operatorname {MEG} $ MEG for short) set of G. The $ \operatorname {MEG} $ MEG number, denoted by $ \operatorname {meg}(G) $ meg ⁡ (G) , is the size of the smallest $ \operatorname {MEG} $ MEG set of G. In this paper, we obtain the exact values of the $ \operatorname {MEG} $ MEG numbers for radix triangular mesh networks, Sierpiński graphs, Sierpiński gasket graphs and Sierpiński generalized graphs. [ABSTRACT FROM AUTHOR]

The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

Multi-path and multi-hop routing are multi-objective optimization problems involving multiple constraints that need to be addressed in the current scenario in wireless sensor networks. The routing process is challenging due to the constrained energy resources and transmission bandwidth. The conventional strategies possess shortcomings, like, as high computing complexity, extensive problem-solving time, complexity in achieving optimal values, and ease of falling into local solutions. Hence, the aim is to propose a hybrid metaheuristic algorithm, known as a multi-objective optimized multi-path and multi-hop routing algorithm (MMMRA). It incorporates the chimp optimization algorithm (COA) for determining the optimal multi-path route based on multi-objective function and ant colony optimization for determining the optimal multi-hop routing. The proposed MMMRA is implemented using NS-2 and to evaluate the performance, nine various scenarios are considered. The MMMRA is validated using different performance measures and compared with other benchmark algorithms. The simulation results indicate that the MMMRA exhibits percentage improvement in terms of residual energy by 1.63%, 4.96%, 6.89%, 7.51%, and 9.67% over IPSMT, BIM2RT, SCP, PSOBS, and RDICMR algorithms respectively. Moreover, the HND and FND of the MMMRA algorithm perform better in all three scenarios (center, corner, and outside positions of sink node), especially when the sink node is placed at the center position, the HND of MMRA shows a percentage improvement by 24% and 12.73% over IPSO–GWO, and COA–HGS algorithms respectively. Similarly, the FND of MMRA shows percentage improvement by 21.05% and 9.5% over IPSO–GWO, and COA–HGS algorithms respectively. [ABSTRACT FROM AUTHOR]

In this study, we present a novel interpolation scheme for chimera simulations in CFD that treats flows with discontinuities. This scheme is suitable for interpolation over polyhedral meshes using only scattered data and without the use of stencils. During the interpolation data transfer among overset meshes, both solution variables and their gradients are communicated and then used for the interpolation process. First, the overset topology problem in partitioned polyhedral meshes is addressed, and then a new interpolation algorithm for generally discontinuous fields is introduced. Then, we describe how the gradient approximation of the Finite Volume (FV) method is utilized in order to construct bounded approximations on values on the direction of discontinuities and enhance the accuracy of the low order Nearest Neighbor value (NNV) algorithm. The performance of the proposed algorithm is quantified and validated in various test cases, together with a comparison with NNV. Finally, scalability tests are presented to prove computational efficiency. The method is proved to be highly accurate in propagation cases and performs well in unsteady two-phase problems executed using parallel architectures. [ABSTRACT FROM AUTHOR]

Many types of objects are crowdedly distributed on the surface of remote sensing scenes. Global semantics with object mixture and small training samples potentially cause difficulties in the classification of remote sensing scenes. The description of them often requires crucial parts of the scenes and corresponding discriminative features, especially as the convolutional neural network (CNN) goes deeper. In this paper, we propose a novel Location-Aware Multi-code Generator network (LAM-GAN) that incorporates multiple latent codes as the input to a generator network. This network is designed to train from scratch and recover most details of the input (real) image in a principled way. Meanwhile, multiple latent codes are reversely updated using K cluster centres located by the subsequently proposed part co-location module. By doing so, the global features of the real-fake image pair and part-level features are stacked and fed to a joint part classification network for discriminative classification. This approach makes it easier to induce the semantic concepts in a remote sensing scene. With this formulation, our approach generates an internal compact representation of the scene and enables weakly supervised part co-localization. The proposed method provides a unified framework for not only generating high-quality fake images but also facilitating the remote sensing scene classification task. We evaluated LAM-GAN on several benchmark datasets, and the experiment results demonstrate that the proposed method is more effective than previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Router nodes placement (RNP) is an important issue in the design and implementation of wireless mesh networks (WMN). This is known as an P-hard problem, which cannot be solved using conventional algorithms. Consequently, approximate optimization strategies are commonly used to solve this problem. With heavy node density and wide-area WMNs, solving the RNP problem using approximation algorithms often faces many difficulties, therefore, a more effective solution is necessary. This motivated us to conduct this work. We propose a new method for solving the RNP problem using reinforcement learning (RL). The RNP problem is modeled as an RL model with environment, agent, action, and reward are equivalent to the network system, routers, coordinate adjustment, and connectivity of the RNP problem, respectively. To the best of our knowledge, this is the first study that applies RL to solve the RNP problem. The experimental results showed that the proposed method increased the network connectivity by up to 22.73% compared to the most recent methods. [ABSTRACT FROM AUTHOR]

Resin-bonded bridges (RBBs) are a minimally invasive and aesthetically pleasing treatment modality. However, their frequent failure has posed challenges for both dental professionals and patients. This necessitates the exploration of innovative strategies to enhance the longevity of RBBs. This study aimed to assess the bond strength of a mesh bridge fabricated using computer-aided design and three-dimensional (3D) printing technology in comparison to the traditional aluminum oxide sandblasting method. A total of 48 lower incisors were embedded in acrylic bases according to a standardized computer-generated model to receive 24 metal RBBs. The two groups underwent distinct metal surface treatments: the 3D mesh novel design and sandblasting with aluminum oxide particles sized at 250.00 µm. The bond strength of the bridges was evaluated, and statistical analysis was performed using the independent samples t-test with a significance level set at α = 0.05. The findings revealed a significant difference between the two methods (p < 0.001). The 3D mesh design exhibited a mean bond strength of 387.89 ± 24.15 N, while the sandblasting technique yielded a mean value of 161.46 ± 31.25 N. In summary, the 3D mesh design substantially enhanced the bond strength of RBBs compared to the traditional sandblasting technique. [ABSTRACT FROM AUTHOR]

• A study about the performance of the MLS, Spline and IDW methods in hydromechanical transfer data problems is realized. • An adaptation of the compact support size based on a minimum number of target points is proposed. • The results show that the compact support size adaptation is very advantageous for the MLS in hydromechanical analyses. • IDW method shows a competitive although it is not conservatively. The investigation of the combined effect of diverse physical phenomena in engineering problems through multiphysics numerical analyses is a trend in many challenging engineering problems. To increase the efficiency of the numerical procedures, multiphysics analyses can be carried out under uncoupled or pseudo-coupled approaches, which frequently require the transfer of data between two non-matching spatial discretizations. Hydromechanical analyses are often executed through pseudo-coupling schemes once each phenomenon requires a different level of discretization or even different numerical approaches. Using appropriate interpolation methods that guarantee the accuracy and conservation of the results is challenging. Methods based on mesh-free interpolation have been broadly used since they show good accuracy and conservation and are topology independent. This work studies the performance and influence of three mesh-free interpolation methods on the response in pseudo-coupled hydromechanical simulations with different meshes. The Moving Least Squares reconstruction, the Spline Interpolation method, and the Inverse Distance Weighting method are assessed regarding accuracy and conservation. An auto-search of the compact support radius scheme based on a minimum number of source points inside the compact support is proposed. The numerical results show the high dependency of the transfer quality on the compact support size. The proposed support size adaptation is shown to be advantageous for the moving least squares method. The inverse distance average method is a promising method for hydromechanical problems. [ABSTRACT FROM AUTHOR]

This paper proposes a rolling bearing fault diagnosis method based on the enhanced channel attention network. The vibration signals are collected via a wireless sensor node, which are input to the neural network, and channel attention block is used to strengthen the assignment of important features, so that the attention of network is paid onto the critical fault information. Furthermore, channel attention block and residual convolution block are combined to form an enhanced channel attention network to extract the detail features. Experiment results show that the model can achieve 100% recognition accuracy for rolling bearings in various working conditions. It is also shown that the proposed new learning algorithm can provide a higher diagnosis accuracy than those state-of-the-arts in a strong disturbance environment, which reflects better robustness and generalisation ability. [ABSTRACT FROM AUTHOR]

This study introduces QMNN, a quantum mesh neural network model incorporating a Quantum Neural Network Expectation (QNNE) circuit, and evaluates its performance on MNIST and CIFAR-10 datasets. QMNN employs innovative quantum mesh architecture with increased random layers, while the QNNE circuit, inspired by Circuit 15, integrates extra Hadamard gates for enhanced feature extraction. Implemented using PennyLane libraries, the model demonstrates superior accuracy in training and testing on both datasets compared to classical neural networks, emphasizing its effectiveness in image classification. While classic methods like CNNs may achieve higher accuracy, the significance lies in QMNN's ability, along with the QNNE circuit, to improve accuracy without introducing additional optimization parameters. The observed quantum advantage suggests that strong entanglement between qubits may enhance pixel correlations, highlighting the potential of quantum-inspired models in advancing image diagnostics. Conclusively, this study provides a stepping stone for further research into the synergy between quantum computing and neural networks, showcasing the promising role of QMNN in augmenting image classification accuracy. The results indicate notable improvements, with MNIST testing accuracy reaching 92.80% and CIFAR-10 testing accuracy achieving 36.90%, demonstrating the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM. [ABSTRACT FROM AUTHOR]

Mesh adaptation is crucial in numerical simulation, providing optimal resource allocation for accurately capturing physical phenomena. However, when applied to Computational Fluid Dynamics (CFD) problems with complex multi-scale properties, existing adaptation methods face huge challenges due to the high computational cost of solving auxiliary partial differential equations (PDEs) and the difficulty in aligning the flow features with mesh geometric features. In this work, an end-to-end data-driven mesh adaptation framework, Flow2Mesh, is proposed to address these challenges by adopting a hybrid modeling strategy to construct the mapping from pixelated flow-fields to graph-based meshes. It achieves a rapid and accurate one-step mesh adaptation via a perceptual feature network (PFN) and a mesh movement network (MMN). PFN extracts the global perceptual features from flow-fields to enhance flow feature representation and mesh resolution independence. In MMN, these features are utilized to deform the initial mesh to a topology-invariant adaptive mesh by a proposed physically driven mesh convolutional network. It considers the inherent mesh geometric information for efficient node feature aggregation and alignment of mesh density with a flow-field structure. To generate high-quality adaptive meshes, various mesh-related losses are designed to regularize the mesh movement and alleviate the mesh tangling. Experiments in CFD scenarios demonstrate the generalization of our model to different design parameters and mesh configurations. It takes three orders of magnitude less time to generate similar meshes than the PDE-based method. The results exhibit the potential of Flow2Mesh to be a flexible and reliable tool for rapid mesh adaptation in scientific and industrial fields. [ABSTRACT FROM AUTHOR]

Polynomial interpolation is an important component of many computational problems. In several of these computational problems, failure to preserve positivity when using polynomials to approximate or map data values between meshes can lead to negative unphysical quantities. Currently, most polynomial-based methods for enforcing positivity are based on splines and polynomial rescaling. The spline-based approaches build interpolants that are positive over the intervals in which they are defined and may require solving a minimization problem and/or system of equations. The linear polynomial rescaling methods allow for high-degree polynomials but enforce positivity only at limited locations (e.g., quadrature nodes). This work introduces open-source software (HiPPIS) for high-order data-bounded interpolation (DBI) and positivity-preserving interpolation (PPI) that addresses the limitations of both the spline and polynomial rescaling methods. HiPPIS is suitable for approximating and mapping physical quantities such as mass, density, and concentration between meshes while preserving positivity. This work provides Fortran and Matlab implementations of the DBI and PPI methods, presents an analysis of the mapping error in the context of PDEs, and uses several 1D and 2D numerical examples to demonstrate the benefits and limitations of HiPPIS. [ABSTRACT FROM AUTHOR]

We summarize the fabrication strategies of metallic meshes (MMs) with hard or soft templates, which produce regular-patterned or random-patterned meshes, emphasizing large scale production technology free of templates would promote the advancement of MMs. Mechanisms and strategies are highlighted in terms of enhancement in optoelectronic properties, thermal, chemical, and mechanical stability that are crucial for improving environment adaptivity of MMs. [Display omitted] Metallic meshes (MMs) are promising for replacement of traditional brittle metal oxide conductors to serve as flexible transparent devices for applications such as environment, energy, display, and human–machine-environment interface. The diversities of MMs in materials, fabrication mechanisms, geometrics and performances extend their applications, however the systematization of this field is rarely concerned. Here, this article summarizes fabrication strategies of MMs in terms of using hard or soft templates, which produce regular-patterned meshes or random-patterned meshes, emphasizing large scale production technology free of templates would lead the development of MMs. Secondly, mechanisms and strategies are highlighted in terms of enhancement of MMs in optoelectronic properties, thermal, chemical, and mechanical stability that are crucial for adapting different application scenarios. Accordingly, the representative applications of MMs in filters, shielding materials, nanogenerators, supercapacitors, solar cells, light-emitting devices, electroluminescent devices, smart windows, thermal management equipment, sensors, and touch panels are introduced, reflecting their respective advantages in diverse applications. Finally, promising solutions to balance the optoelectronic properties and the stability and durability of MMs are proposed to adapt to complex and extreme applications. It is convinced that this review can provide a comprehensive understanding to guide and promote the future development of flexible optoelectronic devices. [ABSTRACT FROM AUTHOR]

This article explores the implementation of high-accuracy GPS-denied ad hoc localization. Little research exists on ad hoc ultra-wideband-enabled localization systems with mobile and stationary nodes. This work aims to demonstrate the localization of bicycle-modeled robots in a non-static environment through a mesh network of mobile, stationary robots, and ultra-wideband sensors. The non-static environment adds a layer of complexity when actors can enter and exit the node's field of view. The method starts with an initial localization step where each unmanned ground vehicle (UGV) uses the surrounding, available anchors to derive an initial local or, if possible, global position estimate. The initial localization uses a simplified implementation of the iterative multi-iteration ad hoc localization system (AHLos). This estimate was refined using an unscented Kalman filter (UKF) following a constant turn rate and velocity magnitude model (CTRV). The UKF then fuses the robot's odometry and the range measurements from the Decawave ultra-wideband receivers stationed on the network nodes. Through this position estimation stage, the robot broadcasts its estimated position to its neighbors to help the others further improve their localization estimates and localize themselves. This wave-like cycle of nodes helping to localize each other allows the network to act as a mobile ad hoc localization network. [ABSTRACT FROM AUTHOR]

Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) have recently become a new popular method for solving forward and inverse problems governed by partial differential equations. However, in the flow around airfoils, the fluid is greatly accelerated near the leading edge, resulting in a local sharper transition, which is difficult to capture by PINNs. Therefore, PINNs are still rarely used to solve the flow around airfoils. In this study, we combine physical-informed neural networks with mesh transformation, using a neural network to learn the flow in the uniform computational space instead of physical space. Mesh transformation avoids the network from capturing the local sharper transition and learning flow with internal boundary (wall boundary). We successfully solve inviscid flow and provide an open-source subsonic flow solver for arbitrary airfoils. Our results show that the solver exhibits higher-order attributes, achieving nearly an order of magnitude error reduction over second-order finite volume method (FVM) on very sparse meshes. Limited by the learning ability and optimization difficulties of the neural network, the accuracy of this solver will not improve significantly with mesh refinement. Nevertheless, it achieves comparable accuracy and efficiency to second-order FVM on fine meshes. Finally, we highlight the significant advantage of the solver in solving parametric problems, as it can efficiently obtain solutions in the continuous parameter space about the angle of attack. [ABSTRACT FROM AUTHOR]

In wireless mesh networks (WMNs) the traffic-aware reliable gateway selection is the key for enhancing the quality of service (QoS) to improve the network performance. In this paper, we have proposed the novel traffic aware reliable gateway selection (TARGS) scheme to improve the QoS in WMNs. In the TARGS scheme, the aggregated traffic at a particular Mesh Router (MR) is calculated using two QoS parameters that are connecting degree and interface queue length. Based on the aggregated traffic, the candidate internet gateways (CIGs) are selected in the region where a high amount of traffic is generated. Internet gateways (IGWs) are chosen based on the reliability value, which is obtained for each CIG using the path tracing method. Simulation results show that the proposed scheme has outperformed two existing schemes in the literature and obtained lower average latency and traffic load and higher throughput than the existing schemes. [ABSTRACT FROM AUTHOR]

We present a rank statistic adaptive multi-stage pruning method to find lightweight neural networks for 3D human mesh recovery while minimizing accuracy drop. We observe that some feature maps often have prominent low-rank patterns regardless of input human images. Furthermore, even after pruning, feature channels that should have been pruned according to pruning criteria frequently re-appear in test time. From these observations, we design rank statistic adaptive multi-stage pruning; thereby, we can prune more filters with recovering mesh reconstruction accuracy. We demonstrate that, for DenseNet-121, 60.0% of parameters and 67.9% of FLOPs are saved while maintaining comparable accuracy to that of the original full model. This is a notable improvement compared to the competing method based on the L1 filter pruning, where the error is increased by 17.55% at the same pruning rate. [ABSTRACT FROM AUTHOR]

In numerical simulations such as computational fluid dynamics simulations or finite element analyses, mesh quality affects simulation accuracy directly and significantly. Smoothing is one of the most widely adopted methods to improve unstructured mesh quality in mesh generation practices. Compared with the optimization-based smoothing method, heuristic smoothing methods are efficient but yield lower mesh quality. The balance between smoothing efficiency and mesh quality has been pursued in previous studies. In this paper, we propose a new smoothing method that combines the advantages of the heuristic Laplacian method and the optimization-based method based on the deep reinforcement learning method under the Deep Deterministic Policy Gradient framework. Within the framework, the actor artificial neural network predicts the optimal position of each interior free node with its surrounding ring nodes. At the same time, a critic-network is established and takes the mesh quality as input and outputs the reward of the action taken by the actor-network. Training of the networks will maximize the cumulative long-term reward, which ends up maximizing the mesh quality. Training and validation of the proposed method are presented both on 2-dimensional triangular meshes and 3-dimensional surface meshes, which demonstrates the efficiency and mesh quality of the proposed method. Finally, numerical simulations on perturbed meshes and smoothed meshes are carried out and compared which prove the influence of mesh quality on the simulation accuracy. [ABSTRACT FROM AUTHOR]

In simulations involving fluid-structure interactions, the partitioned coupling approach allows calculating the fluid flow and the structural displacement using different solvers, which are coupled by a structure-to-fluid algorithm. This partitioned scheme can result in non-conforming meshes at the solid-fluid interface, especially when the fluid and the structure meshes contain elements of different characteristic sizes. In this context, a mesh-interpolation technique is required to transmit the nodal information from one mesh to the other. This paper focuses on comparing radial basis functions and k-nearest neighbours interpolations on fluid-structure interaction test cases. Indeed, those are fast and flexible techniques requiring no topological information other than relative distances between nodes, allowing a straightforward transfer of nodal data between non-conforming meshes. This feature is particularly favourable in the Particle Finite Element Method (PFEM), where remeshing procedures result in significant changes of the finite element discretisation at the fluid-structure interface. Accordingly, our contribution compares the interpolations in a PFEM/FEM partitioned scheme, using 2D and 3D benchmark problems. [ABSTRACT FROM AUTHOR]

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement. [ABSTRACT FROM AUTHOR]

A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values. [ABSTRACT FROM AUTHOR]

Ice-sheet flow models capable of accurately projecting their future mass balance constitute tools to improve flood risk assessment and assist sea-level rise mitigation associated with enhanced ice discharge. Some processes that need to be captured, such as grounding-line migration, require high spatial resolution (under the kilometer scale). Conventional ice flow models mainly execute on central processing units (CPUs), which feature limited parallel processing capabilities and peak memory bandwidth. This may hinder model scalability and result in long run times, requiring significant computational resources. As an alternative, graphics processing units (GPUs) are ideally suited for high spatial resolution, as the calculations can be performed concurrently by thousands of threads, processing most of the computational domain simultaneously. In this study, we combine a GPU-based approach with the pseudo-transient (PT) method, an accelerated iterative and matrix-free solution strategy, and investigate its performance for finite elements and unstructured meshes with application to two-dimensional (2-D) models of real glaciers at a regional scale. For both the Jakobshavn and Pine Island glacier models, the number of nonlinear PT iterations required to converge a given number of vertices (N) scales in the order of O(N1.2) or better. We further compare the performance of the PT CUDA C implementation with a standard finite-element CPU-based implementation using the price-to-performance metric. The price of a single Tesla V100 GPU is 1.5 times that of two Intel Xeon Gold 6140 CPUs. We expect a minimum speedup of at least 1.5 times to justify the Tesla V100 GPU price to performance. Our developments result in a GPU-based implementation that achieves this goal with a speedup beyond 1.5 times. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions. The insights gained will benefit efforts to diminish spatial resolution constraints at higher computing performance. The higher computing performance will allow for ensembles of ice-sheet flow simulations to be run at the continental scale and higher resolution, a previously challenging task. The advances will further enable the quantification of model sensitivity to changes in upcoming climate forcings. These findings will significantly benefit process-oriented sea-level-projection studies over the coming decades. [ABSTRACT FROM AUTHOR]

Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models. [ABSTRACT FROM AUTHOR]

Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m², respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m², respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens. [ABSTRACT FROM AUTHOR]

Foucaud et al. recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. For every edge e of G and a set M ⊆ V (G) , M is a distance-edge-monitoring (DEM for short) set if there are a vertex x of M and a vertex y of G such that e belongs to all shortest paths between x and y. The DEM number dem (G) is the smallest size of such a set in G. The vertices of M represent distance probes in a network modeled by G ; when the edge e fails, the distance from x to y increases, and thus we are able to detect the failure. In this paper, we study Erdös–Gallai-type problems for DEM numbers of general graphs. The exact values or bounds of dem (G) for radix n -triangular mesh networks and hexagonal networks are also given. [ABSTRACT FROM AUTHOR]

Transparent conductive electrodes (TCEs) are indispensable components of various optoelectronic devices such as displays, touch screen panels, solar cells, and smart windows. To date, the fabrication processes for metal mesh‐based TCEs are either costly or having limited resolution and throughput. Here, a two‐step surface energy‐directed assembly (SEDA) process to efficiently fabricate high resolution silver meshes is introduced. The two‐step SEDA process turns from assembly on a functionalized substrate with hydrophilic mesh patterns into assembly on a functionalized substrate with stripe patterns. During the SEDA process, a three‐phase contact line pins on the hydrophilic pattern regions while recedes on the hydrophobic non‐pattern regions, ensuring that the assembly process can be achieved with excellent selectivity. The necessity of using the two‐step SEDA process rather than a one‐step SEDA process is demonstrated by both experimental results and theoretical analysis. Utilizing the two‐step SEDA process, silver meshes with a line width down to 2 µm are assembled on both rigid and flexible substrates. The thickness of the silver meshes can be tuned by varying the withdraw speed and the assembly times. The assembled silver meshes exhibit excellent optoelectronic properties (sheet resistance of 1.79 Ω/□, optical transmittance of ≈92%, and a FoM value of 2465) as well as excellent mechanical stability. The applications of the assembled silver meshes in touch screen panels and thermal heaters are demonstrated, implying the potential of using the two‐step SEDA process for the fabrication of TCEs for optoelectronic applications. [ABSTRACT FROM AUTHOR]

Efficient, accurate and reliable segmentation of femurs from CT-scans is of major importance for patient-specific autonomous finite element analysis (AFE) to determine bone's stiffness and strength. We present a fully automated segmentation algorithm for whole and partial femurs with or without tumors, and clinical applications of the AFE [1] in clinical practice. The segmentation is based on an U-Net convolutional neural network, resulting a 3D mask representing the desired femur in a CT scan. It is robust, independent of the scanning parameters such as slice spacing, pixel size, scanner manufacturer or the femoral length available in the scan. The U-Net was trained on 178 manually segmented femurs (23,721 images) and tested on 43. The performance evaluation resulted in a Dice similarity score (DSC) of 0.9924, intersection over union (IoU) of 0.9849, Hausdorff distance of 4.3315 mm and symmetric average surface distance (ASD) of 0.1326 mm. The algorithm is competitive with the best state-of-the-art femoral segmentation methodologies available. Based on the segmentation an automatic p-FE mesh is generated and physiological boundary conditions representing sidewise falls or stance are being applied automatically to improve the performance of the AFE described in [1]. New examples of the usage of the AFE in endocrinology and orthopedic oncology demonstrate this disruptive technology in actual clinical practice. We present the use of AFE for predicting hip fracture risk in the elderly population due to a sidewise fall and the identification of patients who require a prophylactic surgery due to metastatic tumors in their femurs. • Fully automated U-Net segmentation for femurs from a clinical CT-scan is presented. • The U-net is independent of scanning parameters. • The U-net is an integral part of an autonomous FEA (AFE) of femurs. • Performance: DSC of 0.9908, intersection over union (IoU) of 0.9817. • Application demonstrated for patients with femoral tumors or osteoporosis. [ABSTRACT FROM AUTHOR]

The thermosensitive transient receptor potential (TRP) channels are well-known as bio-thermometers with specific temperature thresholds and sensitivity. However, their precise structural origins are still mysterious. Here, graph theory was used to test how the temperature-dependent non-covalent interactions as identified in the 3D structures of thermo-gated TRPV3 could form a systematic fluidic grid-like mesh network with the constrained thermo-rings from the biggest grids to the smallest ones as necessary structural motifs for the variable temperature thresholds and sensitivity. The results showed that the heat-evoked melting of the biggest grids may control the specific temperature thresholds to initiate channel gating while the smaller grids may be required to secure heat efficacy. Together, all the grids along the lipid-dependent minimal gating pathway may be necessary to change with molar heat capacity for the specific temperature sensitivity. Therefore, this graph theory-based grid thermodynamic model may provide an extensive structural basis for the thermo-gated TRP channels. [ABSTRACT FROM AUTHOR]

Power-performance constraints have been the key driving force that motivated the microprocessor industry to bring unique design techniques in the past two decades. The rising demand for high-performance microprocessors increases the circuit complexity and data transfer rate, resulting in higher power consumption. This work proposes a set of energy recycling resonant pulsed flip-flops to reuse some of the dissipated energy using series inductor–capacitor (LC) resonance. Moreover, this work also presents wideband clocking architectures that use series LC resonance and an inductor tuning technique. By employing pulsed resonance, the switching power dissipated is recycled back. The inductor tuning technique aids in reducing the skew, increasing the robustness of the clock networks. This new resonant clocking architecture saves over 43% power and 90% reduced skew in clock tree networks and saves 44% power and 90% reduced skew in clock mesh networks, clocking a range of 1–5 GHz frequency, compared to conventional primary–secondary flip-flop-based clock networks. Implementation of resonant clock architectures on standard clock network benchmarks depicts 66% power savings and 6.5 × reduced skew while using the proposed pulsed resonant flip-flop and saves 64% power and 12.7 × reduced skew while using the proposed resonant true single-phase clock (TSPC) flip-flop. [ABSTRACT FROM AUTHOR]

Our long-term project involves performing general-purpose computation on 2D amorphous computing media, which consist of arbitrary many, small, identical processing elements that are homogeneously spread in 2D Euclidian space, and that communicate locally in space. While the minimal assumptions on hardware provide the fascinating perspective of arbitrary large computing power, they also make programming notoriously difficult. Furthermore, our project involves simulating objects extended in 2D-space, called "blobs". Maintaining the connectedness of blobs while they move in space adds another layer of difficulty since it demands to process the topology of 2D space. This paper describes a new parallel data structure that can simplify the programming task, in this context. In computer graphics, processing related to 2D topology is performed by using triangle meshes. We consider synchronous media whose underlying network is also a triangle mesh. Our data structure, derived from computer graphics, is anchored on that mesh so that its operations can be compiled on the medium. More precisely, our compiler produces a circuit of logic gates, which enables a high-performance simulation, in the case of crystalline media (Cellular Automata). We demonstrate the expressiveness of the data structure's operation by using an incremental and modular programming style. We program, first small, then larger building-block functions, and re-use them. Blobs are implemented and re-used to compute the Voronoï diagram. What is the scope of the data-structure? This poses the question of whether there exists a universal set of primitives able to program any processing specified only in terms of 2D-geometry. [ABSTRACT FROM AUTHOR]

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs. [ABSTRACT FROM AUTHOR]

A textured urban 3D mesh is an important part of 3D real scene technology. Semantically segmenting an urban 3D mesh is a key task in the photogrammetry and remote sensing field. However, due to the irregular structure of a 3D mesh and redundant texture information, it is a challenging issue to obtain high and robust semantic segmentation results for an urban 3D mesh. To address this issue, we propose a semantic urban 3D mesh segmentation network (MeshNet) with sparse prior (SP), named MeshNet-SP. MeshNet-SP consists of a differentiable sparse coding (DSC) subnetwork and a semantic feature extraction (SFE) subnetwork. The DSC subnetwork learns low-intrinsic-dimensional features from raw texture information, which increases the effectiveness and robustness of semantic urban 3D mesh segmentation. The SFE subnetwork produces high-level semantic features from the combination of features containing the geometric features of a mesh and the low-intrinsic-dimensional features of texture information. The proposed method is evaluated on the SUM dataset. The results of ablation experiments demonstrate that the low-intrinsic-dimensional feature is the key to achieving high and robust semantic segmentation results. The comparison results show that the proposed method can achieve competitive accuracies, and the maximum increase can reach 34.5%, 35.4%, and 31.8% in mR, mF1, and mIoU, respectively. [ABSTRACT FROM AUTHOR]

Intracellular transport of cargoes in the cell is essential for the organization and functioning cells, especially those that are large and elongated. The cytoskeletal networks inside large cells can be highly complex, and this cytoskeletal organization can have impacts on the distance and trajectories of travel. Here, we experimentally created microtubule networks with varying mesh sizes and examined the ability of kinesin-driven quantum dot cargoes to traverse the network. Using the experimental data, we deduced parameters for cargo detachment at intersections and away from intersections, allowing us to create an analytical theory for the run length as a function of mesh size. We also used these parameters to perform simulations of cargoes along paths extracted from the experimental networks. We find excellent agreement between the trends in run length, displacement, and trajectory persistence length comparing the experimental and simulated trajectories. [ABSTRACT FROM AUTHOR]

Proportional reactive power sharing among the distributed generators (DGs) is still a challenge in an islanded microgrid. The disparity in output voltage of each DG is the main cause of the error in reactive power sharing in DGs. The output voltage difference of DGs is caused by unequal line impedances and connected local loads. In order to reduce the error in reactive power sharing among the DGs, a Robust Volt-VAr control scheme (RVVCS) with an adaptive nominal voltage is proposed in this study. Since the suggested strategy is entirely decentralized, it does not need knowledge of feeder impedance or network configurations. Also, it does not require any communication link or optimization technique. The technique proposed is tested on several network configurations, including various loads, radial networks, reconfigured networks, and mesh networks, and is found satisfactory in each case. In one of the many cases presented in this study, with n q = 3 e - 4 V/VAr, β = 0.2, β add = 0.002 and local as well as common load is connected, the suggested RVVCS minimises the error in reactive power output of D G 1 , D G 2 and D G 3 to (42.68%, - 8.92%, - 33.82%), respectively, compared to the error obtained in RDCS (72.21 %, - 15.61%, - 56.6%), SADCS (72.6%, - 16%, - 56.6%) and CDCS (243.74%, - 69.87%, - 173.87%). A similar reduction pattern in reactive power output is also observed in all the case studies performed in this research. The stability of the proposed strategy is investigated in terms of eigenvalue analysis. The practicability of the proposed technique is validated in time-domain simulation in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Applications running in a large and complex manycore system can significantly benefit from adopting the dataflow model of computation. In a dataflow execution environment, a thread can run only if all its required inputs are available. While the potential benefits are large, it is not trivial to improve resource utilization and energy efficiency by focusing on dataflow thread execution models (i.e., the ways specifying how the threads adhering to a dataflow model of computation execute on a given compute/communication architecture). This paper proposes and implements a hardware-software co-design-based dataflow threads management framework. It works at the Network-on-Chip (NoC) level and consists of three stages. The first stage focuses on a fast and effective thread distribution policy. The next stage proposes an approach that adds reconfigurability to a 2D mesh NoC via customized instructions to manage the dataflow thread distribution. Finally, a 2D mesh and ring-based hybrid NoC is proposed for better scalability and higher performance. This work can be considered a primary reference framework from which extensions can be carried out. [ABSTRACT FROM AUTHOR]