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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]

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]

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]

(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]

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]

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]

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]

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]

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]

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]

This study aimed to evaluate the mesh selectivity of gillnets used in olive flounder (Paralichthys olivaceus) fishing by examining the distribution of total length (TL) across various mesh sizes. A marine fishing experiment was conducted at Gajin Harbor in Goseong County, Gangwon Province, employing experimental gear with four different mesh sizes: 90, 105, 135, and 150 mm. Mesh selectivity analysis involved a comparative assessment of selectivity curve methods through normal, log-normal, and bi-normal functions. Among these, the bi-normal function demonstrated the best fit for estimating the mesh selectivity curve within the SELECT model. The critical finding was the net size at which 50% of the minimum landing size (35 cm in TL) of olive flounder was selected, ranging from 108.7 to 121.1 mm. According to the bi-normal function curve, this size was estimated as 114.0 mm. Our findings indicate that using a mesh size larger than the theoretically estimated size in mesh selectivity analysis can further decrease the catch of immature individuals. These findings provide essential data for devising strategies to efficiently utilize and manage olive flounder resources. [ABSTRACT FROM AUTHOR]

Reconstructing 3D human mesh from monocular images has been extensively studied. However, the existing non-parametric reconstruction methods are inefficient when modeling vertex relationship concerning human information due to they generally adopt an uniform template mesh. To this end, this paper proposes a novel hypergraph-based human mesh hierarchical representation that enables the expression of vertices at body, parts, and vertices perspectives, corresponding to global, local and individual, respectively. And then a novel HyperGraph Attention-based human mesh reconstruction network (HGaMRNet) is put forward in turn, which mainly consists of two modules and can efficiently capture human information from different granularities of the mesh. Specifically, the first module, Body2Parts, decouples a body into local parts, and leverages Mix-Attention (MAT) based feature encoder to learn visual cues and semantic information of the parts for capturing complex human kinematic relationships from monocular images. The second module, Part2Vertices, transfers part features to the corresponding vertices through an adaptive incidence matrix, and utilizes a HyperGraph Attention network to update the vertex features. This is conductive to learning the fine-grained morphological information of a human mesh. All in one, supported by the hypergraph-based hierarchical representation of the human mesh, HGaMRNet balances the effects of neighbor vertex from different levels properly and eventually promotes the reconstruction accuracy of human mesh. Experiments conducted on both Human3.6M and 3DPW datasets show that HGaMRNet outperforms most of the existing image-based human mesh reconstruction methods. [Display omitted] • We propose a novel hypergraph-based human mesh hierarchical representation • We introduce a HyperGraph Attention-based human mesh reconstruction network. [ABSTRACT FROM AUTHOR]

Relief patterns represent a surface characteristic that can be seen as the 3D counterpart of the texture concept in 2D images. Such characteristic is well distinct from the 3D object shape but represents a good information source to recognize the object itself. The majority of state-of-the-art techniques for 2D images rely on convolution-based filtering so, the idea of extending such techniques to the mesh manifold domain is quite intriguing as much as challenging. In this paper, we propose a novel approach based on Graph Neural Networks for 3D mesh relief pattern classification. To this end, we designed a bi-level architecture that learns on data structures computed thanks to a mesh resampling algorithm that allows us to represent local surface patches uniformly, while keeping a consistent points order. The local mesh structures are represented by SpiderPatches , that aim to capture local features of the 3D mesh surface, providing a fine-grained rich representation of the relief patterns; global structures are instead captured by MeshGraphs , whose nodes are SpiderPatches, representing the mesh at a macroscopic level. We tested our architecture using SpiderPatches and MeshGraphs on the original meshes of the SHREC'17 and SHREC'20 relief patterns track datasets, showing superior performance to that reported in the literature using a comparable experimental setting. [Display omitted] • A new and original method for relief patterns classification on mesh manifold. • A bi-level graph attention network architecture. • A SpiderPatch surface structure for local mesh description. • A MeshGraph structure for mesh description at higher level of detail. [ABSTRACT FROM AUTHOR]

We present an anisotropic hp -mesh adaptation strategy using a continuous mesh model for discontinuous Petrov-Galerkin (DPG) finite element schemes with optimal test functions, extending our previous work [1] on h -adaptation. The proposed strategy utilizes the built-in residual-based error estimator of the DPG discretization to compute both the polynomial distribution and the anisotropy of the mesh elements. In order to predict the optimal order of approximation, we solve local problems on element patches, thus making these computations highly parallelizable. The continuous mesh model is formulated either with respect to the error in the solution, measured in a suitable norm, or with respect to certain admissible target functionals. We demonstrate the performance of the proposed strategy using several numerical examples on triangular grids. [ABSTRACT FROM AUTHOR]

This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration. [ABSTRACT FROM AUTHOR]

To achieve autonomous anti‐jamming routing for wireless mesh networks (WMNs) under the threat of unknown removable jammers, a Q‐learning anti‐jamming routing algorithm using the improved upper‐confidence‐bound (UCB) algorithm is proposed in this paper. Specifically, the WMNs anti‐jamming routing path finding problem is first modelled as a Markov decision process. Then, to address the slow‐convergence anti‐jamming strategy induced by the drawback that the preset parameters uniquely determine the length of the traditional Q‐learning exploration process, a discounted‐UCB‐tuned algorithm (Dis‐UCB1‐tuned) is adopted as the decision algorithm to replace the ε‐greedy or softmax algorithm in traditional Q‐learning. Finally, the simulation results demonstrate the superiority of the proposed anti‐jamming scheme over the UCB‐based Q learning(UCBQ) algorithm, Dyna‐Q algorithm, and the traditional Q‐learning algorithm. To elaborate, the proposed scheme can fast converge to a superior anti‐jamming routing without any prior knowledge of jamming environment, and its performance is also independent with the preset parameter. [ABSTRACT FROM AUTHOR]

Over 20 million hernias are operated on globally per year, with most interventions requiring mesh reinforcement. A wide range of such medical devices are currently available on the market, most fabricated from synthetic polymers. Yet, searching for an ideal mesh is an ongoing process, with continuous efforts directed toward developing upgraded implants by modifying existing products or creating innovative systems from scratch. In this regard, this review presents the most frequently employed polymers for mesh fabrication, outlining the market available products and their relevant characteristics, further focusing on the state-of-the-art mesh approaches. Specifically, we mainly discuss recent studies concerning coating application, nanomaterials addition, stem cell seeding, and 3D printing of custom mesh designs. [ABSTRACT FROM AUTHOR]

On the path to exascale the landscape of computer device architectures and corresponding programming models has become much more diverse. While various low-level performance portable programming models are available, support at the application level lacks behind. To address this issue, we present the performance portable block-structured adaptive mesh refinement (AMR) framework P arthenon, derived from the well-tested and widely used A thena++ astrophysical magnetohydrodynamics code, but generalized to serve as the foundation for a variety of downstream multi-physics codes. P arthenon adopts the K okkos programming model, and provides various levels of abstractions from multidimensional variables, to packages defining and separating components, to launching of parallel compute kernels. P arthenon allocates all data in device memory to reduce data movement, supports the logical packing of variables and mesh blocks to reduce kernel launch overhead, and employs one-sided, asynchronous MPI calls to reduce communication overhead in multi-node simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD x86 CPUs, IBM Power9 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale on Frontier (the first TOP500 exascale machine), the miniapp reaches a total of 1.7 × 1013 zone-cycles/s on 9216 nodes (73,728 logical GPUs) at ≈ 92 % weak scaling parallel efficiency (starting from a single node). In combination with being an open, collaborative project, this makes Parthenon an ideal framework to target exascale simulations in which the downstream developers can focus on their specific application rather than on the complexity of handling massively-parallel, device-accelerated AMR. [ABSTRACT FROM AUTHOR]

In combat network, the rational use of information communication support force is related to the efficiency of information flow of the combat network and has a profound impact on the generation of the operational capability. Aiming at the optimization of OODA loop, the requirement of information communication support force in combat network is studied. Firstly, the evaluation standard of OODA operation efficiency is determined, and the network is optimized by using the genetic algorithm based on the characteristics of combat network; According to the degree and clustering of each node in the optimized combat network, the information and communication support force requirements of combat entities and network overall is analyzed. Experiments show that the information and communication support force requirements of combat entities are not only related to their importance, but also affected by entity types. The information and communication support force should meet the construction requirements of the combat network including three types of subnets, including two types of star networks and one type of mesh networks, and the three types of subnets shall be connected in an orderly manner. The above conclusions provide theoretical support for the construction of high-capability combat networks. [ABSTRACT FROM AUTHOR]

A decentralized power distribution network consisting of smart microgrids introduces opportunities to trade with energy called transactive energy. However, research studies in the existing literature suggest that several standardized information models for TE do not meet the network architecture's reliability, flexibility, and security requirements. This limitation is mainly due to the static nature of traditional IP infrastructure. To achieve these requirements in the network architecture, this study investigates the optimized application of software-defined network architecture for transactive energy in smart microgrid systems. Through literature research, unique design approaches in an SDN architecture are identified that improve the reliability, flexibility, and security of the SDN architecture. These design approaches include a decentralized controller network layout, redundant link configuration, a mesh network topology, and data encryption. The proposed solution uniquely combines these design approaches into a single optimized SDN solution for TESMS. To validate the improvements of the findings from the literature research, each design approach is simulated in this study using Mininet SDN emulator and AnyLogic system simulation software. The proposed solution is then applied to a use-case scenario that shows the improvements required for TESMS. The use-case scenario shows significant improvement in the data path uptime. An improvement of 0.27% is achieved, which equates to a 2 h per month increase in the data path uptime. The results of the simulation show that the proposed SDN architecture improves the reliability and flexibility of a traditional SDN network. Furthermore, enabling encryption between the nodes improves the security of the SDN architecture. [ABSTRACT FROM AUTHOR]

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications. [ABSTRACT FROM AUTHOR]

Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β -Ginibre point process (β -GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β -GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β , UAV intensity ρ , and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh. [ABSTRACT FROM AUTHOR]

High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may lead to unnecessary storage and computational burdens, while also struggling to capture clear, sharp, and continuous edges. This paper argues that the key to high-fidelity reconstruction lies in preserving sharp features. Therefore, we introduce a novel sharp-feature-preserving reconstruction framework based on primitive detection. It includes an improved deep-learning-based primitive detection module and two novel mesh splitting and selection modules that we propose. Our framework can accurately and reasonably segment primitive patches, fit meshes in each patch, and split overlapping meshes at the triangle level to ensure true sharpness while obtaining lightweight mesh models. Quantitative and visual experimental results demonstrate that our framework outperforms both the state-of-the-art learning-based primitive detection methods and traditional reconstruction methods. Moreover, our designed modules are plug-and-play, which not only apply to learning-based primitive detectors but also can be combined with other point cloud processing tasks such as edge extraction or random sample consensus (RANSAC) to achieve high-fidelity results. [ABSTRACT FROM AUTHOR]

Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction's median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games. [ABSTRACT FROM AUTHOR]

Pilot contamination due to pilot reuse seriously mitigates the performance of the cell-free massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot assignment scheme that employs user clustering and graph coloring (UC-GC) to reduce pilot contamination. The proposed method consists of two steps: firstly, we utilize AP selection to classify all users; secondly, we assign pilots to users with more severe pilot contamination using the graph coloring algorithm and then assign pilots to the remaining users. The numerical simulation results show that the proposed scheme outperforms existing pilot assignment schemes and significantly improves throughout with low complexity. [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 (1 km or better). Conventional ice flow models may need significant computational resources because these models mainly execute Central Processing Units (CPUs), which lack massive parallelism capabilities and feature limited peak memory bandwidth. On the other side of the spectrum, Graphics Processing Units (GPUs) are ideally suited for high spatial resolution as the calculations at every grid point can be performed concurrently by thousands of threads or parallel workers. In this study, we combine GPUs with the pseudo-transient (PT) method, an accelerated iterative and matrix-free solving approach, and investigate its performance for finite elements and unstructured meshes applied to two-dimensional (2-D) models of real glaciers at a regional scale. For both Jakobshavn and Pine Island glacier models, the number of nonlinear PT iterations to converge for a given number of vertices N scales in the order of O(N1.2) or better. We compared the performance of PT CUDA C implementation with a standard finite-element CPU-based implementation using the metric: price and power consumption to performance. The single Tesla V100 GPU is 1.5 times the price of the two Intel Xeon Gold 6140 CPU processors. The power consumption of the PT CUDA C implementation was approximately one-seventh of the standard CPU implementation for the test cases chosen in this study. We expect a minimum speed-up of >1.5 to justify the Tesla V100 GPU price to performance. We report the performance (or the speedup) across glacier configurations for degrees of freedom (DoFs) tested to be >1.5 on a Tesla V100. This study is a first step toward leveraging GPU processing power for accurate polar ice discharge predictions. The insights gained from this study will benefit efforts to diminish spatial resolution constraints at increased computing speed. The increased computing speed will allow running ensembles of ice-sheet flow simulations at the continental scale and at high resolution, previously not possible, enabling quantification of model sensitivity to changes in future climate forcings. These findings will be significantly beneficial for process-oriented and sea-level-projection studies over the coming decades. [ABSTRACT FROM AUTHOR]

With the development of high-performance computers, it is necessary to develop efficient parallel algorithms in the field of computational fluid dynamics (CFD). In this study, a novel parallel communication strategy based on asynchronous and packaged communication is proposed. The strategy implements an aggregated communication process, which requires only one communication in each iteration step, significantly reducing the number of communications. The correctness and convergence of the novel strategy are demonstrated from both theoretical and experimental perspectives. And based on the real vehicle CHN-T model with 140 million meshes, a detailed performance comparison and analysis is performed for the novel strategy and the traditional strategy, showing that the novel strategy has significant advantages in terms of scalability. Finally, the strong scalability and weak scalability tests are carried out separately for the CHN-T model. The strong scaling efficiency can reach 74% with 10.5 billion meshes and 256,000 cores. The weak scaling parallel efficiency can reach 90% with 10 billion meshes and 179,000 cores. This research work has laid an important foundation for the development of the fast design of aircraft and cutting-edge numerical methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze a novel digital technique to quantify the distal tooth displacement and derotation angle produced by the Carriere Motion Appliance (CMA). Twenty-one patients with a class II molar and canine relationship underwent orthodontic treatment with CMA. All patients were exposed before (STL1) and after the CMA placement (STL2), submitted to a digital impression, and afterwards, data were uploaded to specific cephalometric software to allow automatic mesh network alignment of the STL digital files. Subsequently, the distal tooth displacement of the upper canines and first upper molars, as well as the derotation angle of the first upper molars were analyzed using the Pearson correlation coefficient (ρ). Repeatability and reproducibility were analyzed using Gage R&R statistical analysis. An increase in canine displacement was correlated with an increase in contralateral canine displacement (ρ = 0.759; p < 0.000). An increase in canine displacement was correlated with an increase in molar displacement (ρ = 0.715; p < 0.001). An increase in upper first molar displacement was correlated with an increase in the contralateral upper first molar displacement (ρ = 0.609; p < 0.003) and the canine displacement (ρ = 0.728; p < 0.001). The distal tooth displacement showed a repeatability of 0.62% and reproducibility of 7.49%, and the derotation angle showed a repeatability of 0.30% and reproducibility of 0.12%. The novel digital measurement technique is a reproducible, repeatable, and accurate method for quantifying the distal tooth displacement of the upper canine and first upper molar, as well as the derotation angle of the first upper molars after using CMA. [ABSTRACT FROM AUTHOR]

The increasing number of renewable energy sources (RESs) connected to power grids contributes to the emergence of not only balancing problems but also technical ones, such as the overloading of power lines. If renewable sources with a high generation level are planned to be connected in the area under consideration, then a large number of significant overloads should be expected, especially during contingency analysis. As a rule, high-voltage networks have a mesh topology, which is why the concept of using advanced mathematical algorithms was developed, with the help of which the resulting threats can be eliminated. This article presents a proposal for a new method of eliminating line overloads and determining the currently available nodal generation levels. Its innovation is a new method of eliminating problems related to the capacity of power grids. The high efficiency of the method results from the appropriately defined response of properly selected RES sources to the state of network congestion. The problem under consideration is illustrated with the example of a modified IEEE 118-bus test network. In order to eliminate line overloads, the article proposes a two-stage approach. In the first step, the sources that are most responsible for the occurring overloads are determined. In the second step, a metaheuristic algorithm is used to solve a nonlinear optimisation problem with constraints. This task involves reducing the power generated in the sources selected in the previous step in such a way that the resulting line overloads are eliminated, and, at the same time, the total value of the curtailed generation is minimal. [ABSTRACT FROM AUTHOR]

Device‐to‐device (D2D) and full duplex (FD) technologies demonstrate immense potentials to improve the spectral efficiency of wireless networks. However, their synergistic combination to reap added benefits is proved to be technically challenging. This letter investigates the throughput‐maximization resource allocation problem for FD D2D communications underlaying cellular networks. The problem is decoupled into two subproblems, namely power allocation and channel assignment, which are solved by using a novel quadratic transform based scheme and the Kuhn–Munkres algorithm, respectively. Simulation results show that the proposed algorithm outperforms existing ones in terms of overall system throughput. [ABSTRACT FROM AUTHOR]

Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh–star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS. [ABSTRACT FROM AUTHOR]

In the past, wounds were treated with natural materials, but modern wound dressings include functional elements to expedite the process of healing and to improve skin recovery. Due to their exceptional properties, nanofibrous wound dressings are now the most cutting-edge and desirable option. Similar in structure to the skin's own extracellular matrix (ECM), these dressings can promote tissue regeneration, wound fluid transportation, and air ductility for cellular proliferation and regeneration owing to their nanostructured fibrous meshes or scaffolds. Many academic search engines and databases, such as Google Scholar, PubMed, and Sciencedirect, were used to conduct a comprehensive evaluation of the literature for the purposes of this investigation. Using the term "nanofibrous meshes" as a keyword, this paper focuses on the importance of phytoconstituents. This review article summarizes the most recent developments and conclusions from studies on bioactive nanofibrous wound dressings infused with medicinal plants. Several wound-healing methods, wound-dressing materials, and wound-healing components derived from medicinal plants were also discussed. [ABSTRACT FROM AUTHOR]

Fissures and holes widely exist in rock mechanics engineering, and, at present, their failure mechanisms under complex compress and shear stress states have not been well recognized. In our work, a fracture mark, ξ, is introduced, and the kernel function of the smoothed-particle hydrodynamics (SPH) is then re-written, thus realizing the fracture modelling of the rock media. Then, the numerical models containing the fissures and holes are established, and their progressive failure processes under the compress and shear stress states are simulated, with the results showing that: (1) the improved SPH method can reflect the dynamic crack propagation processes of the rock masses, and the numerical results are in good agreement with the previous experimental results. Meanwhile, the improved SPH method can get rid of the traditional mesh re-division problems, which can be well-applied to rock failure modeling; (2) the hole shapes, fissure angles, fissure lengths, fissure numbers, and confining pressure all have great impacts on the final failure modes and peak strengths of the model; and (3) in practical engineering, the rock masses are in the 3D stress state, therefore, developing a high performance 3D SPH program and applying it to engineering in practice will be of great significance. [ABSTRACT FROM AUTHOR]