Central Patch Research Articles

Reducing the Mutual Coupling of Cylindrical Circular Microstrip Antennas (CCMAs) Array Using EBG Structure

A theoretical study to design a conformal microstrip antennas was introduced in this work. Conformal microstrip antennas define antennas which can be conformed to a certain shape or to any curved surface. It is used in high-speed trains, aircraft, defense and navigation systems, landing gear and various communications systems, as well as in body wearable. Conformal antennas have some advantages such as a wider-angle coverage compared to flat antennas and low radar cross-sectional (RCS) and they are suitable for using in Radome. The main disadvantage of these antennas is the narrow bandwidth. The FDTD method is extremely useful in simulating complicated structures because it allows for direct integration of Maxwell's equations depending on time. The 1x2 cylindrical circular microstrip antennas array is designed and simulated vertically via Finite Difference Time Domain (FDTD) method where can directive antenna be achieved through antennas array design. Mutual coupling between the antennas in the array and the different separation between them were studied. The circular patch is excited by a probe feed method for several reasons including providing less spurious radiation from the probe current, in addition to the simplicity in theoretical engineering installation and practical manufacturing. It is well known that the values of the coupling are decreased as the distance separation increased. Cylindrical circular microstrip antenna with resonant frequency operating is 3.5GHz for mode, several parameters like return loss, band width, and input impedance are calculated. Also, for isolated coupling, mutual coupling coefficients, directivity gain, for different separations between the centers of the two adjacent circular patches in terms the wave length operating are calculated. Moreover, the electromagnetic band gap EBG structure is used for reducing the mutual coupling created by the surface waves in order to enhance the antenna's performance in an array has become smaller than before. The proposed EBG is a three triangular-shape equal sides metallic structure, utilizing the inter-element spacing in an array. The less value of for the spacing between the two centers of patches is . BW percentage increased to 34.3% and the directivity is enhanced also. Additionally, simulations were done using MATLAB 2017b.

Density‐dependent dispersal strategy of pollinator moderates the adverse effect of habitat loss on plant reproduction: An integrated model based on pollinators' behavioural response

Abstract Major challenges for plant conservation are predicting the effect of habitat loss on pollination success and plant reproduction potential. Most studies report that pollinator movement is affected by quantitative and spatial characteristics of landscapes. However, little is known about the role of pollinator movement, impacted by floral volatiles and intraspecies interaction, on plant reproduction in fragmented landscapes. To clarify the effect of pollinator movement on plant reproduction relative to habitat loss, we developed an integrated model incorporating pollinator's foraging response with its dispersal process mediated by a density‐dependent dispersal (DDD) strategy. This model performed better in capturing behaviour response of pollinators than do current methods. The integrated model was verified with field results of pollinator visitation and plant reproduction of saltcedar Tamarix chinensis inhabiting the Yellow River Delta, and then was compared against a dispersal strategy called density‐independent dispersal (DID). The model was applied to landscapes with various non‐habitat percentage (NHP) to explore the effect of habitat loss on plant reproduction. Results suggested that saltcedar populations differ in their responses to habitat loss, which depended on the spatial scales considered. At landscape scale, increasing NHP significantly inhibited dispersion extent of floral volatiles and therefore reduced pollinator visitation and subsequent seed production, especially when NHP exceeded the critical threshold of 0.6. However, at patch scale, comparing with DID strategy, the DDD strategy enabled pollinators to increase their utilization of flowers by 43.42% and 6.79% in low‐density and distant plant patches, whereas their utilization was reduced by 7.75% and 2.24% in high‐density and central patches, respectively. Plant reproduction was improved correspondingly in low‐density and distant patches under different NHPs. Consequently, habitat loss inhibits the volatile dispersion and interferes the foraging success of pollinators, a major factor influencing plant reproduction at landscape scale. At patch scale, adaptive utilization of pollinators exhibiting DDD strategy alleviates the negative effect of habitat loss on plant production, and maintain plant population persistence. Since pollinator behavioural response is critical to plant reproduction, we recommend the use of the here presented integrated model to assess the impact of habitat loss on plant reproduction. Read the free Plain Language Summary for this article on the Journal blog.

Semantic segmentation of superficial layer in intracoronary optical coherence tomography based on cropping-merging and deep learning

Manual labelling and qualitative analysis of lesion tissues in vessel optical coherence tomography (OCT) images are time-consuming and laborious for cardiovascular specialists. To beat these issues, a semantic segmentation method was presented based on deep learning on the OCT image analysis of the human vessel to reduce the diagnosis pressure of cardiovascular doctors. An outer border of ROI was segmented based on the level-set method to obtain the visible superficial layer containing useful information. And then, cropping square patches from the ROI with its patch center pixel inside the ROI and using patches as the input data. To reuse the preceding layers' feature maps, we used the dense block to replace the normal convolution layer, and simultaneously, employed a skip-connection from m the down-sampling path to the up-sampling path to keep the spatial information. With the advantage of SegNet on semantic segmentation, a dense-block-SegNet (DBSegNet) is constructed to complete the pixel-level segmentation. Training and testing were executed on 7 datasets (22, 210 images) to assess the model. Tenfold cross-validation method was implemented to measure the classification outcomes and the semantic segmentation capacity of our model. In deep learning experiment, sensitivities for calcified, fibrous and lipid plaques were 91.81 ± 3.60 %, 92.81 ± 2.72 % and 91.78 ± 1.62 %, respectively. A 3-D volume was created to insert each prediction slice along the depth axis to compute the maximum type number of each pixel in order to identify the final type of each pixel. Post-processing was used to refine the classification findings in order to eliminate classification errors. Semantic segmentation neural network based on the cropped input data and feature reusing was a feasible approach for the vessel tissue pixel-classification of IVOCT image. The proposed method has the potential to become a useful tool for specialists in analysing lesion kinds and determining clinical lesion characteristics (e.g., distribution position, angle and depth).

Синтез адаптивной цилиндрической передачи с арочными зубьями с двумя зонами контакта

Cylindrical gears with arched teeth have the required performance in conditions of both non-rigid machine bodies and inevitable manufacturing and assembly errors. Error compensation is carried out by self-alignment of the wheels, the possibilities of which are limited in gears with one line of engagement. The article investigates an adaptive gear with arched teeth with two contact zones, which provides selfalignment of the wheels, regardless of the angle of misalignment of the teeth in engagement. The most promising in terms of implementation in the industry method of cutting arched teeth with a circular cutter head by the rolling method with a single division on CNC machines. Solved and illustrated by examples of the problem of calculating the parameters of the process of shaping the surfaces of arched teeth, which ensure the passage of two active lines in the meshing of the teeth through the given centers of the contact patches and the law of change of the transfer function required for the implementation of the approximate nature of the meshing.

Schwannoma: Another dermoscopic pitfall?

Schwannoma: Another dermoscopic pitfall?

Hexagonal-Triangular Combinatorial Structure Based Dual-Band Circularly Polarized Patch Antenna for GAGAN Receivers

A multilayer gap-coupled dual band hexagonal microstrip patch antenna is presented for GPS Aided GEO Augmented Navigation (GAGAN) receivers. GAGAN is developed by AAI (Airport Authority of India) and ISRO (Indian Space Research Organization) exclusively for aviation purposes with the GPS frequencies L5 (1176.45 MHz) and L1 (1575.42 MHz). The proposed GAGAN antenna has three trapezoidal and four equilateral triangular patch resonators configured to form a gap-coupled hexagonal patch on the top layer and seven equilateral triangular patches with slots in between to form the middle layer. Both the layers are fed by a single coax feed to achieve a dual band compact microstrip antenna of size 55 mm x 55 mm x 3.2 mm. Instead of designing the hexagonal antenna conventionally from a circular patch antenna, the sides of the equilateral triangular patch are truncated to obtain the proposed compact hexagonal antenna as the triangular antenna occupies less area when compared to circular or square patches for a fixed frequency. To bring in novelty with improved performance, the combinatorial structured configuration has been explored through step-by-step optimization. The aspects of GAGAN requirements like circular polarization with a single input, dual band operation and high gain with gain flatness are achieved with this compact structured antenna when compared to square and circular patch antennas. The combinatorial structures such as perturbation and truncation introduced in the patches are responsible for exciting two near-degenerate orthogonal modes producing circular polarization (CP). A cross slot etched in the center patch results in gain enhancement with a gain above 3.2 dBi over a bandwidth of 20 MHz in both the bands. Five shorting pins are added to achieve high impedance matching and good CP with an axial ratio (AR) below 2 dB. The simulated and measured results are found to be in good agreement with greater than 20 MHz operational bandwidth exhibiting CP with an omni-directional radiation pattern having high cross polar levels of -22 dB. The size reduction achieved in the proposed design is 56.79% when compared to the conventional hexagonal patch antenna. The measured 10 dB impedance bandwidth is 2.7% and 2.1% for L5 and L1 bands respectively with a 3 dB AR bandwidth of 2.3% for L5 and 2.0% for L1, satisfying GAGAN requirements.

Wideband Low-Profile Eight-Port Eight-Wave Annular-Ring Patch Antenna Based on Using Eight Dual-Shorted Dual-Resonant Ring Sectors for 8 × 8 MIMO Mobile Devices

A wideband eight-port eight-wave annular-ring (AR) patch antenna for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> 8 MIMO mobile devices is presented. The AR patch antenna is formed into eight dual-resonant 45o-ring sectors to radiate eight very-low-correlated or generally uncorrelated waves for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> 8 MIMO application. Each ring sector is loaded with two shorting pins and excited by a simple probe feed to generate its half- and quarter-wavelength resonant modes at close frequencies to form a dual-resonant wide operating band. Additionally, all 16 shorting pins of the eight ring sectors have a same distance to the patch center, thereby forming a shorting circle to function like a circular metal wall in the AR patch. This makes the surface currents coupled from the excited ring sector to adjacent sectors to be mainly confined inside the shorting circle. Therefore, with the eight probe feeds located outside the shorting circle in the AR patch, enhanced port isolation is obtained. With the proposed design, the eight-port AR patch antenna having a low profile of 1.4 mm (about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.028\lambda $ </tex-math></inline-formula> at 5.9 GHz) and an outer diameter of 46 mm (about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.91\lambda $ </tex-math></inline-formula> at 5.9 GHz) can generate eight very-low-correlated waves over a wide band of about 5.9-7.2 GHz (fractional bandwidth 20% based on 3:1 VSWR for mobile device application). Also, the proposed antenna has a back ground plane and is especially suitable for on-metal surface application. The use of a single proposed antenna can be conveniently applied for the mobile device or Internet-of-Things (IoT) device to cover such as 5.925-7.125 GHz for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> 8 MIMO operation in the possible new mobile communication spectrum.

Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction.

Intra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of thehead. State-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. The proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free low-resolution reference scan to provide the ground truth orientation. The supervised training utilizes motion simulations based on 28 acquisitions with subject-wise training, validation, and test data splits of 70%, 23%, and 7%. During inference, MoPED is embedded in an iterative DC-driven motion correction algorithm which alternatingly updates estimates of the motion parameters and motion-corrected low-resolution k-space data. The estimated motion parameters are then used to reconstruct the final motion correctedimage. The mean absolute/squared error and the Pearson correlation coefficient were used to analyze the motion parameter estimation quality on in-silico data in a quantitative evaluation. Structural similarity (SSIM), DC error and root mean squared error (RMSE) were used as metrics of image quality improvement. Furthermore, the generalization capability of the network was analyzed on two in-vivo motion volumes with 28 slices each and on one simulated T1-weightedvolume. The motion estimation achieves a Pearson correlation of 0.968 to the simulated ground-truth of the 2433 test data slices used. In-silico results indicate that MoPED decreases the time for the optimization by a factor of around 27 compared to a conventional method and is able to reduce the RMSE of the reconstructions and average DC error by more than a factor of two compared to uncorrected images. In-vivo experiments show a decrease in computation time by a factor of around 20, a RMSE decrease from 0.055 to 0.033 and an SSIM increase from 0.795 to 0.862. Furthermore, contrast independence is demonstrated as MoPED is also able to correct T1-weighted images in simulations without retraining. Due to the model-based correction, no hallucinations wereobserved. Incorporating DL in a model-based motion correction algorithm shows great benefit on the optimization and computation time. The k-space-based estimation also allows a data consistent correction and therefore avoids the risk of hallucinations of image-to-imageapproaches.

Ants disperse seeds farther in habitat patches with corridors

AbstractHabitat fragmentation impacts ecosystems worldwide through habitat loss, reduced connectivity, and edge effects. Yet, these landscape factors are often confounded, leaving much to be investigated about their relative effects, especially on species interactions. In a landscape experiment, we investigated the consequences of connectivity and edge effects for seed dispersal by ants. We found that ants dispersed seeds farther in habitat patches connected by corridors, but only in patch centers. We did not see an effect on the total number of seeds moved or the rate ants detected seeds. Furthermore, we did not see any differences in ant community composition across patch types, suggesting that shifts in ant behavior or other factors increased ant seed dispersal in patches connected by corridors. Long‐distance seed dispersal by ants that requires an accumulation of short‐distance dispersal events over generations may be an underappreciated mechanism through which corridors increase plant diversity.

Design of a Circuit-Free Filtering Metasurface Antenna Using Characteristic Mode Analysis

This communication presents a method of designing filtering metasurface (MTS) antennas, which is to excite the candidate in-band modes and suppress the out-of-band interference ones. The candidate modes are selected depending on the expected radiation patterns and operating band and excited by choosing a reasonable feeding structure. The suppression of interference modes is achieved by controlling the modal currents with the aid of a characteristic mode analysis (CMA). As a result, the MTS works as a radiator and a filter simultaneously. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> array MTS antenna is implemented to demonstrate the concept. Targeting the broadside radiation pattern and 5G operating band, two candidate MTS modes are selected. A ground slot is used to excite them and introduce an extra slot mode. The out-of-band interference modes are eliminated by removing specific patches and etching a split slot on the central patch of the MTS. Moreover, to improve the selectivity at the lower band edge, a probe is added at the center of the antenna. The antenna has a simulated −10 dB impedance bandwidth of 25%, covering the 5G n77 band (3.3–4.25 GHz). The out-of-band suppression level exceeds 27 dB. Thin-band peak gain is 10.9 dBi, while the peak efficiency reaches 95%.

A Joint Bayesian Optimization for the Classification of Fine Spatial Resolution Remotely Sensed Imagery Using Object-Based Convolutional Neural Networks

In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex urban settings. As a result, combining deep learning and object-based image analysis (OBIA) has become a new avenue in remote sensing studies. This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. The proposed classification model achieved the best accuracy, with 0.96 OA, 0.95 Kappa, and 0.96 mIoU in the training area and 0.97 OA, 0.96 Kappa, and 0.97 mIoU in the test area, outperforming several benchmark methods including Patch CNN, Center OCNN, Random OCNN, and Decision Fusion. The analysis of CNN variants within the proposed classification workflow showed that the HybridSN model achieved the best results compared to 2D and 3D CNNs. The 3D CNN layers and combining 3D and 2D CNN layers (HybridSN) yielded slightly better accuracies than the 2D CNN layers regarding geometric fidelity, object boundary extraction, and separation of adjacent objects. The Bayesian optimization could find comparable optimal MRS parameters for the training and test areas, with excellent quality measured by AFI (0.046, −0.037) and QR (0.945, 0.932). In the proposed model, higher accuracies could be obtained with larger patch sizes (e.g., 9 × 9 compared to 3 × 3). Moreover, the proposed model is computationally efficient, with the longest training being fewer than 25 s considering all the subprocesses and a single training epoch. As a result, the proposed model can be used for urban and environmental applications that rely on VHR satellite images and require information about land use.

Emotional scene remembering: A combination of disturbing and facilitating effects of emotion?

An emotion-induced memory trade-off effect is frequently reported when participants have to memorize complex items that include both neutral and emotional features. This bias corresponds to better remembering of central emotional information accompanied by poor performance related to neutral background information. Although the trade-off effect has been mainly associated with attentional bias toward emotional content, findings suggest that other non-attentional cognitive processes could also be involved. The aim of this work was to assess whether emotional effects would be reported apart from their influence on attentional processing in an immediate delay memory task. Three studies were conducted. In Study 1, manipulation of the diffusion quality of emotional content allowed us to select focal emotional pictures vs. diffuse emotional pictures, which prevented attentional focus. The two studies that followed consisted of a recognition task of low- and high-complexity pictures in which we used partial visual cues during the test that could display either the emotional elements (i.e., central patch cues, Study 2) or the peripheral elements (i.e., peripheral patch cues, Study 3) of the focal emotional pictures. Results from Studies 2 and 3 replicated traditional trade-off effects only for high-complexity pictures. In addition, diffuse emotional pictures were associated with lower memory performance than were neutral pictures, suggesting that emotion features could both disturb and enhance (via their attentional effect) encoding processes.

Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco

The Paraguayan Chaco is one of the most rapidly deforested areas in Latin America, mainly due to cattle ranching. Continuously forested windbreaks between agricultural areas and forest patches within these areas are mandatory to minimise the impact that the legally permitted logging has on the ecosystem. Due to the large area of the Paraguayan Chaco, comprehensive in situ monitoring of the integrity of these landscape elements is almost impossible. Satellite-based remote sensing offers excellent prerequisites for large-scale land cover analyses. However, traditional methods mostly focus on spectral and texture information while dismissing the geometric context of landscape features. Since the contextual information is very important for the identification of windbreak gaps and central forests, a deep learning-based detection of relevant landscape features in satellite imagery could solve the problem. However, deep learning methods require a large amount of labelled training data, which cannot be collected in sufficient quantity in the investigated area. This study presents a methodology to automatically classify gaps in windbreaks and central forest patches using a convolutional neural network (CNN) entirely trained on synthetic imagery. In a two-step approach, we first used a random forest (RF) classifier to derive a binary forest mask from Sentinel-1 and -2 images for the Paraguayan Chaco in 2020 with a spatial resolution of 10 m. We then trained a CNN on a synthetic data set consisting of purely artificial binary images to classify central forest patches and gaps in windbreaks in the forest mask. For both classes, the CNN achieved an F1 value of over 70%. The presented method is among the first to use synthetically generated training images and class labels to classify natural landscape elements in remote sensing imagery and therewith particularly contributes to the research on the detection of natural objects such as windbreaks.

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis

Continuous technological growth and the corresponding environmental implications are triggering the enhancement of advanced environmental monitoring solutions, such as remote sensing. In this paper, we propose a new method for the spatial point patterns generation by classifying remote sensing images using convolutional neural network. To increase the accuracy, the training samples are extended by the suggested data augmentation scheme based on the similarities of images within the same part of the landscape for a limited observation time. The image patches are classified in accordance with the labels of previously classified images of the manually prepared training and test samples. This approach has improved the accuracy of image classification by 7% compared to current best practices of data augmentation. A set of image patch centers of a particular class is considered as a random point configuration, while the class labels are used as marks for every point. A marked point pattern is regarded as a combination of several subpoint patterns with the same qualitative marks. We analyze the bivariate point pattern to identify the relationships between points of different types using the features of a marked random point pattern.

The Atlantic Ocean landscape: A basin-wide cluster analysis of the Atlantic near seafloor environment

Landscape maps based on multivariate cluster analyses provide an objective and comprehensive view on the (marine) environment. They can hence support decision making regarding sustainable ocean resource handling and protection schemes. Across a large number of scales, input parameters and classification methods, numerous studies categorize the ocean into seascapes, hydro-morphological provinces or clusters. Many of them are regional, however, while only a few are on a basin scale. This study presents an automated cluster analysis of the entire Atlantic seafloor environment, based on eight global datasets and their derivatives: Bathymetry, slope, terrain ruggedness index, topographic position index, sediment thickness, POC flux, salinity, dissolved oxygen, temperature, current velocity, and phytoplankton abundance in surface waters along with seasonal variabilities. As a result, we obtained nine seabed areas (SBAs) that portray the Atlantic seafloor. Some SBAs have a clear geological and geomorphological nature, while others are defined by a mixture of terrain and water body characteristics. The majority of the SBAs, especially those covering the deep ocean areas, are coherent and show little seasonal and hydrographic variation, whereas other, nearshore SBAs, are smaller sized and dominated by high seasonal changes. To demonstrate the potential use of the marine landscape map for marine spatial planning purposes, we mapped out local SBA diversity using the patch richness index developed in landscape ecology. It identifies areas of high landscape diversity, and is a practical way of defining potential areas of interest, e.g. for designation as protected areas, or for further research. Clustering probabilities are highest (100%) in the center of SBA patches and decrease towards the edges (&amp;lt; 98%). On the SBA point cloud which was reduced for probabilities &amp;lt;98%, we ran a diversity analysis to identify and highlight regions that have a high number of different SBAs per area, indicating the use of such analyses to automatically find potentially delicate areas. We found that some of the highlights are already within existing EBSAs, but the majority is yet unexplored.

Segmentation Method of Cerebral Aneurysms Based on Entropy Selection Strategy.

The segmentation of cerebral aneurysms is a challenging task because of their similar imaging features to blood vessels and the great imbalance between the foreground and background. However, the existing 2D segmentation methods do not make full use of 3D information and ignore the influence of global features. In this study, we propose an automatic solution for the segmentation of cerebral aneurysms. The proposed method relies on the 2D U-Net as the backbone and adds a Transformer block to capture remote information. Additionally, through the new entropy selection strategy, the network pays more attention to the indistinguishable blood vessels and aneurysms, so as to reduce the influence of class imbalance. In order to introduce global features, three continuous patches are taken as inputs, and a segmentation map corresponding to the central patch is generated. In the inference phase, using the proposed recombination strategy, the segmentation map was generated, and we verified the proposed method on the CADA dataset. We achieved a Dice coefficient (DSC) of 0.944, an IOU score of 0.941, recall of 0.946, an F2 score of 0.942, a mAP of 0.896 and a Hausdorff distance of 3.12 mm.

Broadband and high gain circularly polarised truncated corner square patch metasurface antenna using aperture CPW feed

Broadband and the high gain circularly polarised antenna are designed in a single-layered planar substrate. An array of 3 × 3 truncated corner square patches are designed as a metasurface antenna on one side of the substrate and the metasurface antenna is fed by a stub-loaded uniform aperture CPW feed on another side of the substrate. In the metasurface antenna, the size of the central patch is increased for improving the bandwidth of impedance bandwidth and 3 dB axial ratio. Furthermore, the amount of truncation is reduced in the patches surrounding the central patch to enhance the bandwidth of impedance bandwidth and 3 dB axial ratio bandwidth. The designed metasurface antenna has achieved an impedance bandwidth of 38.2% (4.5–6.63 GHz) with an axial ratio bandwidth of 13% (5.0–5.7 GHz). Moreover, the antenna has a peak gain of 10.36dBic. The simulation and measurement results are compared.

A Complex Networks Analysis of Ecological Corridors Classification with ECSuite

The ecological corridors allow protected species to migrate autonomously between the areas defined by the Natura 2000 project. Although corridors bring multiple advantages in theory, in practice their addition increases exponentially the complexity of managing the territory and monitoring the fauna. Through the use of the software suite exposed in this paper, it is possible to study the ecological corridor simply and effectively, thanks to the classification and representation of the central and critical patches. The paper will focus on the structure and functioning of ECGraph, the software that deals with the logic needed to identify the crucial nodes of the corridor thanks to the theories of complex networks. Some considerations have also been added to the description of the software for its future refinement thanks to some metrics not yet implemented. Each step of its functioning will also be represented thanks to ECOverview, a graphic software designed and developed for a simpler representation of the results obtained by ECGraph.

FCHO controls AP2's initiating role in endocytosis through a PtdIns(4,5)P2-dependent switch.

Clathrin-mediated endocytosis (CME) is the main mechanism by which mammalian cells control their cell surface proteome. Proper operation of the pivotal CME cargo adaptor AP2 requires membrane-localized Fer/Cip4 homology domain-only proteins (FCHO). Here, live-cell enhanced total internal reflection fluorescence–structured illumination microscopy shows that FCHO marks sites of clathrin-coated pit (CCP) initiation, which mature into uniform-sized CCPs comprising a central patch of AP2 and clathrin corralled by an FCHO/Epidermal growth factor potential receptor substrate number 15 (Eps15) ring. We dissect the network of interactions between the FCHO interdomain linker and AP2, which concentrates, orients, tethers, and partially destabilizes closed AP2 at the plasma membrane. AP2’s subsequent membrane deposition drives its opening, which triggers FCHO displacement through steric competition with phosphatidylinositol 4,5-bisphosphate, clathrin, cargo, and CME accessory factors. FCHO can now relocate toward a CCP’s outer edge to engage and activate further AP2s to drive CCP growth/maturation.

Oyster Reef Surfaces in the Central Wadden Sea: Intra-Reef Classification and Comprehensive Statistical Description

The Pacific oyster (Magallana gigas) is an invasive species in the Wadden Sea transforming parts of it permanently. M. gigas, as an ecosystem engineer, builds reef structures that are characterized by highly complex and variable surfaces consisting of densely packed, sharp-edged individuals connected with cement-like bonds. To investigate the interactions between reef structure, shape and formation and wave as well as tidal currents, an understanding of the surface roughness is essential. This work reports on observations of oyster reefs for which seven new structural classes (Central Reef, Transitional Zone, Cluster I, Cluster II, Patch I, Patch II, and Garland) are proposed. For each class, high resolution Digital Elevation Models (DEMs) have been elaborated based on Structure-from-Motion (SfM) photogrammetry and analyzed using spatial statistics. By determining probability density functions (PDFs), vertical porosity distributions, abundances, orientations and second-order structure functions (SSFs), topographical parameters that influence the hydraulic bed roughness have been determined. The results suggest, that by applying the structural classification and their distinct topographical roughness parameters, the oyster reef surfaces can be described appropriately accounting for their complexity. The roughness accounts to a total roughness height kt = 103 ± 15 mm and root-mean-square roughness height krms = 23 ± 5 mm. These values were found similar across all structural classes, yet the shape of the PDFs reveal differences. With decreasing abundance, the distributions become more positively skewed and are characterized by more extreme outliers. This is reflected in the higher statistical moments, as the skewness ranges between Sk = 0.4–2.1 and the kurtosis between Ku = 2.2–11.5. The analysis of the orientations and the SSFs confirms anisotropic behavior across all structural classes. Further, the SSFs reveal the oyster shells as significant roughness elements with exception of Cluster I and II, where the clusters are identified as significant roughness elements. The provided set of topographical roughness parameters enhances the knowledge of oyster reef surfaces and gives insights into the interactions between biogenic structure and surrounding hydrodynamics. The new intra-reef classification allows for more accurate determination of the overall roughness as well as the population dynamics of the habitat forming oyster. Combined with hydraulic measurements, the results can be used to estimate the hydraulic bed roughness induced by the oyster reef surfaces.