Dynamic Patch Research Articles

Multi-generational dispersal and dynamic patch occupancy reveals spatial and temporal stability of seascapes

The success of non-native species (NNS) invasions depends on patterns of dispersal and connectivity, which underpin genetic diversity, population establishment and growth. In the marine environment, both global environmental change and increasing anthropogenic activity can alter hydrodynamic patterns, leading to significant inter-annual variability in dispersal pathways. Despite this, multi-generational dispersal is rarely explicitly considered in attempts to understand NNS spread or in the design of management interventions. Here, we present a novel approach to quantifying species spread that considers range expansion and network formation across time using the non-native Pacific oyster, Magallana gigas (Thunberg 1793), as a model. We combined biophysical modelling, dynamic patch occupancy models, consideration of environmental factors, and graph network theory to model multi-generational dispersal in northwest Europe over 13 generations. Results revealed that M. gigas has a capacity for rapid range expansion through the creation of an ecological network of dispersal pathways that remains stable through time. Maximum network size was achieved in four generations, after which connectivity patterns remained temporally stable. Multi-generational connectivity could therefore be divided into two periods: network growth (2000−2003) and network stability (2004–2012). Our study is the first to examine how dispersal trajectories affect the temporal stability of ecological networks across biogeographic scales, and provides an approach for the assignment of site-based prioritisation of non-native species management at different stages of the invasion timeline. More broadly, the framework we present can be applied to other fields (e.g. Marine Protected Area design, management of threatened species and species range expansion due to climate change) as a means of characterising and defining ecological network structure, functioning and stability.

Simultaneous image patch attention and pruning for patch selective transformer

Vision transformer models provide superior performance compared to convolutional neural networks for various computer vision tasks but require increased computational overhead with large datasets. This paper proposes a patch selective vision transformer that effectively selects patches to reduce computational costs while simultaneously extracting global and local self-representative patch information to maintain performance. The inter-patch attention in the transformer encoder emphasizes meaningful features by capturing the inter-patch relationships of features, and dynamic patch pruning is applied to the attentive patches using a learnable soft threshold that measures the maximum multi-head importance scores. The proposed patch attention and pruning method provides constraints to exploit dominant feature maps in conjunction with self-attention, thus avoiding the propagation of noisy or irrelevant information. The proposed patch-selective transformer also helps to address computer vision problems such as scale, background clutter, and partial occlusion, resulting in a lightweight and general-purpose vision transformer suitable for mobile devices.

ZOZI-Seg: A transformer and UNet cascade network with Zoom-Out and Zoom-In scheme for aortic dissection segmentation in enhanced CT images

Background & objectiveAortic dissection (AD) is a serious condition requiring rapid and accurate diagnosis. In this study, we aimed to improve the diagnostic accuracy of AD by presenting a novel method for aortic segmentation in computed tomography images that uses a combination of a transformer and a UNet cascade network with a Zoom-Out and Zoom-In scheme (ZOZI-seg). MethodsThe proposed method segments each compartment of the aorta, comprising the true lumen (TL), false lumen (FL), and thrombosis (TH) using a cascade strategy that captures both the global context (anatomical structure) and the local detail texture based on the dynamic patch size with ZOZI schemes. The ZOZI-seg model has a two-stage architecture using both a “3D transformer for panoptic context-awareness” and a “3D UNet for localized texture refinement.” The unique ZOZI strategies for patching were demonstrated in an ablation study. The performance of our proposed ZOZI-seg model was tested using a dataset from Asan Medical Center and compared with those of existing models such as nnUNet and nnFormer. ResultsIn terms of segmentation accuracy, our method yielded better results, with Dice similarity coefficients (DSCs) of 0.917, 0.882, and 0.630 for TL, FL, and TH, respectively. Furthermore, we indirectly compared our model with those in previous studies using an external dataset to evaluate its robustness and generalizability. ConclusionsThis approach may help in the diagnosis and treatment of AD in different clinical situations and provide a strong basis for further research and clinical applications.

Proactive river corridor definition: Recommendations for a process‐based width optimization approach illustrated in the context of the coastal Pacific Northwest

AbstractWe propose a holistic approach to define a river corridor as the minimum space needed to sustain key river functions based on an understanding of the desired functions of that corridor and the processes governing channel and floodplain formation. Giving such space is a fundamental nature‐based solution to river management, as it allows the river to use its own energy to maintain flood conveyance and habitat function. The review of existing river corridor concepts shows that these often focus on one or two potential functions of a river corridor and may not be well suited as tools to optimize eco‐geomorphic river function. We argue that evaluating the effects of river corridor width on multiple processes can provide an objective means to optimize delineation in areas where development encroaches onto floodplains and channel migration zones. Key processes are linked to channel migration and include floodplain rejuvenation, emergence of a dynamic patch mosaic of riparian habitat that sustains a functioning large wood cycle, and effects of constriction and confinement on channel dynamics and morphology. Quantification of these processes for an example river shows the most rapid gains for habitat and flood protection values up to the threshold for planform‐controlled conditions and an asymptotically reducing rate of gain in function above that threshold. For the example river, the threshold width approaching that asymptote is substantially more than its constrained condition but much less than the width of the floodplain and channel migration zone, offering a compromise for managing rivers with developed floodplains.This article is categorized under: Engineering Water > Sustainable Engineering of Water Water and Life > Nature of Freshwater Ecosystems Water and Life > Conservation, Management, and Awareness

PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation

The lightweight MLP-based decoder has become increasingly promising for semantic segmentation. However, the channel-wise MLP cannot expand the receptive fields, lacking the context modeling capacity, which is critical to semantic segmentation. In this paper, we propose a parametric-free patch rotate operation to reorganize the pixels spatially. It first divides the feature map into multiple groups and then rotates the patches within each group. Based on the proposed patch rotate operation, we design a novel segmentation network, named PRSeg, which includes an off-the-shelf backbone and a lightweight Patch Rotate MLP decoder containing multiple Dynamic Patch Rotate Blocks (DPR-Blocks). In each DPR-Block, the fully connected layer is performed following a Patch Rotate Module (PRM) to exchange spatial information between pixels. Specifically, in PRM, the feature map is first split into the reserved part and rotated part along the channel dimension according to the predicted probability of the Dynamic Channel Selection Module (DCSM), and our proposed patch rotate operation is only performed on the rotated part. Extensive experiments on ADE20K, Cityscapes and COCO-Stuff 10K datasets prove the effectiveness of our approach. We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.

Tumor growth with nutrients: Regularity and stability

In this paper, we study a tumor growth model with nutrients. The model presents dynamic patch solutions due to the incompressibility of the tumor cells. We show that when the nutrients do not diffuse and the cells do not die, the tumor density exhibits regularizing dynamics thanks to an unexpected comparison principle. Using the comparison principle, we provide quantitative L 1 L^1 -contraction estimates and establish the C 1 , α C^{1,\alpha } -boundary regularity of the tumor patch. Furthermore, whenever the initial nutrient n 0 n_0 either lies entirely above or entirely below the critical value n 0 = 1 n_0=1 , we are able to give a complete characterization of the long-time behavior of the system. When n 0 n_0 is constant, we can even describe the dynamics of the full system in terms of some simpler nutrient-free and parameter-free model problems. These results are in sharp contrast to the observed behavior of the models either with nutrient diffusion or with death rate in tumor cells.

Low Profile Dynamic Patch Antenna Array With High-Intensity Radiation Fields (HIRF) Protection

A 1-bit low-profile patch antenna array integrating beam scanning and adaptive high-intensity radiation fields (HIRF) protection is proposed in this paper. Each antenna element consists of two dielectric layers, two radiation patches, a ground plane, and a feeding network. By electronically controlling a pair of coding diodes in the same direction, the antenna element can achieve 180 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> phase difference, producing two logical states, namely “0” and “1”. It has good beam scanning capability in the range of −60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> to 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> . When the proposed antenna is exposed to HIRF, the protective diodes mounted over the grounded coplanar waveguide (GCPW) transmission line are triggered, dramatically reducing the overall gain of the antenna array. By optimizing design, the element possesses a bandwidth of 8.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 9.3 GHz. The grating lobe of the antenna array is suppressed by 17.5 dB thanks to the adoption of the non-uniform feed. The simulation results indicate that the proposed antenna element and array have a high isolation degree of 22.4 dB and 23.2 dB when exposed to HIRF, respectively. Finally, a prototype of the antenna is fabricated, and the validity of the proposed design is experimentally verified. The experimental results are in good agreement with the simulation results, demonstrating the validity of the proposed design.

Spatiotemporal connectivity dynamics in spatially structured populations.

Connectivity is a fundamental concept linking dispersal to the emergent dynamics and persistence of spatially structured populations. Functional measures of connectivity typically seek to integrate aspects of landscape structure and animal movement to describe ecologically meaningful connectedness at the landscape and population scale. Despite this focus on function, traditional measures of landscape connectivity assume it is a static property of the landscape, hence abstracting out the underlying spatiotemporal population dynamics. Connectivity is, arguably, a dynamic property of landscapes, and is inherently related to the spatial distribution of individuals and populations across the landscape. Static representations of connectivity potentially overlook this variation and therefore adopting a dynamic approach should offer improved insights about connectivity and associated ecological processes. Using a large-scale, long-term time series of occupancy data from a metapopulation of water voles Arvicola amphibius, we tested competing hypotheses about how considering the dynamic nature of connectivity improves the ability of spatially explicit occupancy models to recover population dynamics. Iteratively relaxing standing assumptions of connectivity metrics, these models ranged from spatially and temporally fixed connectivity metrics that are widely applied, to the more flexible, but lesser used model that allowed temporally varying connectivity measures that incorporate spatiotemporally dynamic patch occupancy states. Our results provide empirical evidence that demographic weighting using patch occupancy dynamics and temporal variability in connectivity measures are important for describing metapopulation dynamics. We highlight the implications of commonly held assumption in connectivity modelling and demonstrate how they result in different and highly variable predictions of metapopulation capacity. Thus, we argue that the concept of connectivity and its potential applications would benefit from recognizing inherent spatiotemporal variation in connectivity that is explicitly linked to underlying ecological state variables.

Silicon Wearable Body Area Antenna for Speech-Enhanced IoT and Nanomedical Applications

We propose in this paper a reduction in the size of wearable antennas on silicon (Si) for medicinal frameworks and Internet of things (IoT) in various nanoapplications. This research also introduces one more type of dynamic patch antenna designed in favor of speech-enhanced healthcare applications. The most significant impediment to the adoption of smart correspondence and medical services frameworks is voice-enabled IoT. The primary objective of a body area network (BAN) is to give ceaselessly clinical information to the doctors. Actually, wireless body area network is flexible, dense, trivial, and less expensive. On the other hand, the main disadvantage is low efficiency for small printed antenna. Microstrip silicon antenna recurrence is changed because of ecological conditions, distinctive reception apparatus areas, and diverse framework activity modes. By using tunable antenna, the efficiency of bandwidth usage can be increased. Amplifiers are associated with the feed line of antenna in order to build its dynamic range. In this study, a dynamic polarized antenna is constructed, analysed, and attempted for fabrication. The gain of the antenna is 13 ± 2 dB for the frequency range of 390 to 610 MHz. The output of the polarized antenna is roughly 19 dBm. At different environmental conditions, the performance and ability to control the antenna could vary. To achieve stable performance, we have used varactor diode and voltage-controlled diode. This silicon wearable antenna can be fabricated and tested for many medical applications like health monitoring system and pacemakers. Furthermore, micromachining techniques can be used to lower the practical dielectric constant of silicon and hence improve radiation efficiency.

In-and-Out: a data augmentation technique for computer vision tasks

This study focuses on the over-fitting problem in the training process of the deep convolutional neural network model and the problem of poor robustness when the model is applied in an occlusion environment. We propose a unique data augmentation method, In-and-Out. First, the information variance is enhanced through dynamic local operation while maintaining the overall geometric structure of the training image; compared with the global data augmentation method, our method effectively alleviates the overfitting problem of model training and significantly improves the generalization ability of the model. Then through the dynamic information removal operation, the image is hidden according to the dynamic patch generated by multiple parameters. Compared with other information removal methods, our method can better simulate the real-world occlusion environment, thus improving the robustness of the model in various occlusion scenes. This method is simple and easy to implement and can be integrated with most CNN-based computer vision tasks. Our extensive experiments show that our method surpasses previous methods on the Canadian Institute for Advanced Research dataset for image classification, the PASCAL Visual Object Classes dataset for object detection, and the Cityscapes dataset for semantic segmentation. In addition, our robustness experiments show that our method has good robustness to occlusion in various scenes.

Improving Face-Based Age Estimation With Attention-Based Dynamic Patch Fusion

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.

Effects of a mobile disturbance pattern on dynamic patch networks and metapopulation persistence

MotivationCertain early-succession habitats may emerge only at restricted locations following disturbance. Therefore, whether disturbances tend to occur at certain sites or not can significantly affect habitat availability and metapopulation persistence of early-successional habitat specialists. Available models that combine metapopulation and landscape processes do not address how to model mobile, spatially shifting disturbance intensities independent of factors of site suitability. We present a model that allows the study on how a mobile disturbance pattern, of either natural or anthropogenic origin, affects patch network and metapopulation dynamics in realistic, heterogeneous landscapes. MethodsWe simulate metapopulation dynamics using a realistic landscape and varying patch destruction (and turnover) rates. We model the local patch emergence rate as the function of site suitability to patch emergence –a permanent factor–and local disturbance intensity, which we first estimate from empirical data and then simulate using annually updating spatial random fields. Using this model, we test whether and how a mobile disturbance pattern affects metapopulation persistence of the false heath fritillary butterfly (Melitaea diamina). ResultsIn our case study, a mobile disturbance pattern caused new patches to emerge further away from occupied patches over time. This decreased the probability of new patches becoming colonized and thus impaired metapopulation persistence even when the median distance between patches appeared unchanging. However, if disturbances moved to areas that were highly suitable to patch emergence, increased habitat availability could compensate the otherwise detrimental effects of a mobile disturbance pattern. Disturbances that had a moderate degree of mobility had the most uncertain effects to metapopulation persistence. ConclusionsOur modelling approach distinguishes between two processes behind the spatio-temporal pattern and rates of patch emergence–disturbance dynamics and varying site suitability. It enables the use of social and environmental data for forecasting habitat availability for early-succession habitat specialists under alternative future scenarios. It can be applied and developed further to suit multiple study systems. Our case study suggests that for species conservation, it is either beneficial to organize recurring management activities to take place at constant locations, or to gradually shift them towards areas that are highly suitable to patch emergence.

Construction of informatization management platform for large-scale urban terrain dynamic patch surveying

Construction of informatization management platform for large-scale urban terrain dynamic patch surveying

Interference of Illusory Contour Perception by a Distractor.

The visual system is capable of recognizing objects when object information is widely separated in space, as revealed by the Kanizsa-type illusory contours (ICs). Attentional involvement in perception of ICs is an important topic, and the present study examined whether and how the processing of ICs is interfered with by a distractor. Discrimination between thin and short deformations of an illusory circle was investigated in the absence or presence of a central dynamic patch, with difficulty of discrimination varied in three levels (easy, medium, and hard). Reaction time (RT) was significantly shorter in the absence compared to the presence of the distractor in the easy and medium conditions. Correct rate (CR) was significantly higher in the absence compared to the presence of the distractor in the easy condition, and the magnitude of the difference between CRs of distracted and non-distracted responses significantly reduced as task difficulty increased. These results suggested that perception of ICs is more likely to be vulnerable to distraction when more attentional resources remain available. The present finding supports that attention is engaged in perception of ICs and that distraction of IC processing is associated with perceptual load.

Real-time fast fog removal approach for assisting drivers during dense fog on hilly roads

Dense fog is a vital cause of risk in driving perception, especially on a hilly road. In hilly areas, drivers are unable to view the roads prominently and sometimes unable to notice a nearby object properly due to dense fog. The dense fog causes many road accidents and also reduces the speed of the vehicle. In this article, a vision-based real-time fast fog removal approach is proposed to obtain a prominent view of the hilly road during heavy fog by video processing techniques. The proposed approach is established on an atmospheric based scattering model and least filtering technique with a dynamic patch followed by independent histogram equalization built on color channels. First, the atmospheric light is estimated at an interval of 6000 frames using the least filtering technique with a dynamic patch. Then, frames are inverted to estimate the transmission map, and fog-free scenes are recovered. Finally, the independent histogram equalization based on color channel is used to eliminate the haze noise with proper contrast adjustment. The proposed approach can provide a fog-free vision of roads as well as any distant object for any driver in minimum processing time. Several real-time exploratory tests are conducted during heavy fog on hilly roads. Qualitative and quantitative test analysis exposes that the proposed approach provides enhanced performance compared to the latest approaches. After defogging, the distance of visibility rises by more than 92% during dense fog with great accuracy of 97.41%.

Structure constrained image completion by geometric transformation model and dynamic patches

ABSTRACTImage completion aims to fill in a damaged region (hole) using texture form known regions. In this study, a novel image completion method is proposed that can repair a hole region using preserved structure. A geometric transformation model is first proposed to determine the relation between patches. Then, a multi-layer dynamic patch solution is obtained that can both effectively recover damaged structure and expedite the algorithm. To obtain satisfactory results, image completion is reduced to an energy optimization problem. An objective function is established, and the optimal solution is obtained. The proposed method is applied in various scenes, and the results demonstrate that this method has strong potential as an image completion technique.

Autonomous preforming system: automated and sensor-aided handling of dry carbon fibre textiles

Preforming of carbon fibre reinforced plastic (CFRP) parts is one of the most difficult and time consuming manufacturing processes for large aircraft structures. The main challenges are the handling and draping of flexible fabrics into complex curved geometries to achieve the defined fibre angles which are mandatory for the required mechanical properties of the part. At the Centre for Lightweight Production Technology (ZLP) of the German Aerospace Centre (DLR) in Augsburg automation solutions for such manufacturing processes of full scale aircraft structures are developed.This paper presents an approach for a dynamic patch preforming method for dry carbon fibre textiles. The goal was the automation of the process with focus on digitalization. To identify essential automation steps the process chain was analyzed and described. The automation for the dynamic preforming was established with an industrial robot cell and a mechanical end effector. The end effector is capable of deforming to fit complex curvatures and handling of cut pieces regarding the draping simulations. A digitalized model of the end effector was used to simulate its deformation. Furthermore the detection, autonomous handling and movement monitoring of cut pieces is explained. Finally an automated process for a full scale Rear Pressure Bulkhead (RPB) patch preforming was set up and realized.

Both children and adult patients with difficult-to-treat atopic dermatitis have high prevalences of concomitant allergic contact dermatitis and are frequently polysensitized.

BackgroundConcomitant allergic contact dermatitis (ACD) has been described as a possible cause of atopic dermatitis (AD) becoming difficult‐to‐treat. However, contact sensitization in this patient group has barely been studied.ObjectiveTo study the occurrence of ACD in a population of difficult‐to‐treat AD children and adults.MethodsClinical and patch test information of 48 patients with difficult‐to‐treat AD unresponsive to conventional outpatient treatments was gathered retrospectively. We studied prevalence and relevance of common allergens, performed dynamic patch test analysis and assessed occurrence of polysensitization.ResultsIn 48 patients with difficult‐to‐treat AD, 75% (n = 36/48) had a concomitant contact allergy, and 39% (n = 14/36) of these patients were polysensitized. ACD and polysensitization prevalences were equal amongst children and adults. The most frequent and relevant reactions were seen against wool alcohols, surfactants cocamidopropyl betaine and dimethylaminopropylamine, bichromate and fragrance mix I. Dynamic pattern analysis showed these reactions to be mostly allergic and not irritative of nature.ConclusionDifficult‐to‐treat AD patients frequently suffer from concomitant (multiple) contact allergies, and this may be a reason why the AD turns into a difficult‐to‐treat disease. Awareness of this phenomenon is necessary, as pragmatic implementation of allergen avoidance strategies may be helpful in getting disease control in this population.

Structure-preserving image completion with multi-level dynamic patches

In this paper, we present a novel structure-preserving image completion approach equipped with dynamic patches. We formulate the image completion problem into an energy minimization framework that accounts for coherence within the hole and global coherence simultaneously. The completion of the hole is achieved through iterative optimizations combined with a multi-scale solution. In order to avoid abnormal structure and disordered texture, we utilize a dynamic patch system to achieve efficient structure restoration. Our dynamic patch system functions in both horizontal and vertical directions of the image pyramid. In the horizontal direction, we conduct a parallel search for multi-size patches in each pyramid level and design a competitive mechanism to select the most suitable patch. In the vertical direction, we use large patches in higher pyramid level to maximize the structure restoration and use small patches in lower pyramid level to reduce computational workload. We test our approach on massive images with complex structure and texture. The results are visually pleasing and preserve nice structure. Apart from effective structure preservation, our approach outperforms previous state-of-the-art methods in time consumption.

Long-term Outcomes of Natural-process Riparian Restoration on a Regulated River Site: The Rio Grande Albuquerque Overbank Project after 16 Years

In 1998, a riparian restoration demonstration project was initiated with a target of efficiently establishing a dynamic patch mosaic of vegetation communities along a regulated river using available water and sediment and remaining natural hydrological processes. A point bar along the Middle Rio Grande, Albuquerque, New Mexico, dominated by the non-native shrub Elaeagnus angustifolia (Russian olive), was mechanically treated by removing all vegetation and lowering a portion of the bar to allow overbank flooding during typical spring releases from an upstream dam (Cochiti Dam). Side channels and small islands were engineered in the lowered bar to slow flood waters, aid sediment deposition, and add site complexity. After treatment, a high-resolution monitoring grid was installed to track vegetation changes. Following an initial flood in the spring of 1998, over 10,000 cottonwoods per ha naturally established, but densities varied based on the fluvial landforms. Zones that were sufficiently wetted or naturally formed behind large woody debris were the most successful, while the artificial fill zone and the portion of the bar not lowered had the least native riparian tree recruitment. Over 15 years, cottonwood numbers declined through intraspecific competition and beaver browsing at all sites, but they continued to dominate. Natives also dominated a species-rich herbaceous layer, particularly on the lowered sites. The incursion of a new herbaceous invader, Saccharum ravennae (ravennagrass), was an unexpected outcome revealed by the long-term monitoring record. Yet, based on several criteria, the site reflects a successful application of a natural-process approach to restoration that can lead to increased ecosystem complexity and resilience.