Candidate Areas Research Articles

Moult chronology and strategies of sympatric Great (Ardenna gravis) and Sooty (A. grisea) Shearwaters based on stable isotope analysis

Moult is an energetically demanding period, during which flight may be impaired and foraging ranges may become constrained. During the non‐breeding period, GreatArdenna gravisand SootyArdenna griseaShearwaters migrate from South Atlantic breeding colonies to aggregate at North Atlantic feeding grounds. We investigated whether both shearwater species used coastal Newfoundland, Canada, as a moulting area and used stable isotope ratios (δ15N, δ13C) of recently moulted primary feathers (P1, P5, P10) to infer moult location/diet for both species. Moult scores indicated that both species finished their moult (i.e. P6–10) in coastal Newfoundland, which was further corroborated with similar stable isotope ratios for Great (δ15N = 15.17 ± 1.13‰; δ13C = −18.66 ± 0.54‰) and Sooty Shearwaters (15.54 ± 0.74‰; −18.43 ± 0.78‰); however, Sooty Shearwater moult was more advanced relative to that of Great Shearwater. In contrast, isotopic ratios of P1 and P5, which were grown before arriving in coastal Newfoundland, differed between and within species, suggesting divergent locations/diet during early moult. For Great Shearwaters, P1/P5 isotopic ratios were more variable (broader niche breadth) than P10, suggesting that some individuals started moulting in the South Atlantic prior to trans‐equatorial migration, whereas others start moulting in the North Atlantic Ocean. Sooty Shearwaters had two distinct groupings of either higher or lower δ15N in P1/P5, suggesting that individuals began moulting either on the Newfoundland Shelf or further offshore based on comparisons to reference shearwater feathers grown in known locations. These findings illustrate distinct locations and/or diets at the start of primary feather moult, both within and between species, but diets converged when aggregated together at the end of moult in coastal North America, where growing feathers of both species were sampled. More importantly, we identified an important area for both Sooty and Great Shearwaters to complete their moult in coastal Newfoundland. Protecting this moulting area would minimize disturbance and the impacts of threats (e.g. by‐catch) to both species during this energetically demanding period. The area has been suggested previously to be an important candidate area for protection due to annually persistent prey aggregations that can be spatiotemporally delimited based on specific prey habitat requirements.

A Text Location Method for Sign Based on Pixel-level Gradient Ray Images and Edge Contour Mapping

Compared with deep learning methods, traditional image processing methods have lower computational costs and have advantages for typical problems such as sign text, this paper proposes a sign text location method based on pixel-level gradient ray map and edge contour mapping. First, based on image pre-processing, the pixel-level gradient ray map is obtained by using the Stroke Width Transform. Then, using the Maximally Stable Extremal Region method to locate the irregular text candidate area in the ray map. At the same time, add a mask layer to filter the irregular candidate area, and then regularize the candidate area and perform Non-Maximum Suppression processing. Finally, the background non-text candidate area is processed by extracting the mapping of the closed edge contour of the image, and the remaining text area positioning candidate frame coordinates are mapped back to the original picture. The experimental results show that the method is simple and clear, the effect is remarkable, and it has good robustness to the sign of different sizes.

Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach.

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.

Selection of National Park Candidate Areas Based on Spatial Overlap Characteristics of Protected Areas in China

The integration and optimization of protected areas is an important part of the construction of a protected areas system centered around national parks. How to best integrate and optimize protected areas is the most urgent problem in the reform. This paper analyzes the spatial overlap and continuity of protected areas in China on a national scale and proposes a collection of candidate areas for national parks. The results show that ➀ 52.9% of the protected areas overlap, with nature reserves, forest parks and scenic areas showing the most overlap, and the maximum number of overlaps is five. ➁ There are 1145 groups of contiguous protected areas that form continuous boundary areas, accounting for 58.9% of the total number of protected areas analyzed in this paper. Of these continuous zones, 48.55% consists of only two protected areas. There are 51 continuous areas with more than 10 protected areas, showing point continuous and/or patchy continuous boundary characteristics. ➂ According to the identified continuous areas, overlapping degree, protection levels and function, the candidate areas of national parks in China are proposed. Continuous areas with comprehensive ecosystem services, high-intensity protection levels, and high overlap intensity are selected as preliminary candidate areas for national parks. These are further refined based on their co-location with four types of key areas. A total of 41 areas are recommended as potential national parks. These continuous areas are highly consistent with the national “two screens, three belts” strategy, and nine of them are essentially consistent with the current national parks pilot. These results indicate that the recommended areas selected according to this research method are reasonable, and can provide a scientific basis for determining the spatial layout of China’s new protected areas system and the establishment of national parks.

A Robust Checkerboard Corner Detection Method for Camera Calibration Based on Improved YOLOX

Camera calibration plays an important role in various optical measurement and computer vision applications. Accurate calibration parameters of a camera can give a better performance. The key step to camera calibration is to robustly detect feature points (typically in the form of checkerboard corners) in the images captured by the camera. This paper proposes a robust checkerboard corner detection method for camera calibration based on improved YOLOX deep learning network and Harris algorithm. To get high checkerboard corner detection robustness against the images with poor quality (i.e., degradation, including focal blur, heavy noise, extreme poses, and large lens distortions), we first detect the corner candidate areas through the improved YOLOX network which attention mechanism is added. Then, the Harris algorithm is performed on these areas to detect sub-pixel corner points. The proposed method is not only more accurate than the existing methods, but also robust against the types of degradation. The experimental results on different datasets demonstrate its superior robustness, accuracy, and wide effectiveness.

Asphalt Pavement Crack Detection Based on Fusion of Neighborhood and Gradient Salient Features

In order to solve the problem of the rich texture noise and serious misidentification of small cracks in the asphalt pavement crack detection,, an asphalt pavement crack detection method that combines neighborhood and gradient salient features is proposed. Firstly, the grayscale correction and morphological reconstruction are used to reduce the influence of external interference and uneven brightness caused by the bright spot noise in the rich texture, then the neighborhood salient features are extracted according to the salient differences between pixels and their neighborhood, and gradient salient features in different directions are obtained through the steerable filter. With the convolutional fusion of the neighborhood and gradient salient features, preferential directions are chosen to generate the salient map of feature fusion. Secondly, the suspected crack aggregation areas are obtained by the threshold segmentation of salient map. Based on the different geometric characteristics of the aggregation areas, a clustering analysis method is introduced to select the crack candidate area. Finally, the method of searching and locating a regional endpoint is proposed to eliminate the subsets without endpoint aggregation area, the endpoints of different regions are connected to achieve the complete crack extraction. The experimental results of the collected asphalt pavement crack image datasets show that the precision, recall and <italic>F</italic>-measure value are 92.857%, 86.405% and 89.515%, which can effectively detect cracks in asphalt pavement images, especially for small cracks, and provide more accurate crack information for pavement maintenance.

Transcranial direct current stimulation of the premotor cortex aimed to improve hand motor function in chronic stroke patients

ObjectiveTo investigate the effects of single-session premotor and primary motor tDCS in chronic stroke patients with relation to possible inter-hemispheric interactions. MethodsAnodal tDCS of either M1 or premotor cortex of the side contralateral to the paretic hand, cathodal tDCS of the premotor cortex of the side ipsilateral to the paretic hand and sham stimulation were performed in 12 chronic stroke patients with mild hand paresis in a balanced cross-over design. The Jebsen-Taylor Hand Function test, evaluating the time required for performance of everyday motor tasks, was employed. ResultsThe repeated-measure ANOVA with Greenhouse-Geisser correction showed significant influence of the stimulation type (factor SESSION; F(2.6, 28.4) = 47.3, p < 0.001), the test performance time relative to stimulation (during or after tDCS; factor TIME, F(1.0, 11.0) = 234.5, p < 0.001) with higher effect after the stimulation and the interaction SESSION*TIME (F(1.7, 1.2) = 30.5, p < 0.001). All active conditions were effective for the modulation of JTT performance, though the highest effect was observed after anodal tDCS of M1, followed by effects after anodal stimulation of the premotor cortex contralateral to the paretic hand. Based on the correlation patterns, the inhibitory input to M1 from premotor cortex of another hemisphere and an excitatory input from the ipsilesional premotor cortex were suggested. ConclusionThe premotor cortex is a promising candidate area for transcranial non-invasive stimulation of chronic stroke patients.

General Landscape Connectivity Model (GLCM): a new way to map whole of landscape biodiversity functional connectivity for operational planning and reporting

Graph-theoretic approaches are commonly used to map landscape connectivity networks to inform environmental management priorities. We developed the new General Landscape Connectivity Model (GLCM), as a operationally practical way of evaluating and mapping habitat networks to inform conservation priorities and plans. GLCM is built on two complementary metapopulation ecology-based measures: Neighbourhood habitat area (Ni) and habitat link value (Li). Ni is a measure of the amount of connected habitat to each location considering its cross-scale connectivity to neighbouring habitat. The remaining Ni across a region can be reported as an indicator of Ecological Carrying Capacity for wildlife (plants and animals). Li at any location is its contribution to the landscape connectivity of the study region (i.e. which is reported as summed Ni across a region) by virtue of providing the ‘least-cost’ linkages between concentrations of habitat. Mapped Li provides valuable insights into the pattern of a region's habitat network, highlighting functioning habitat corridors and stepping-stones, and candidate areas for conservation and restoration. Due to its foundations in ecological theory and its parsimonious design, GLCM addresses a number of criteria we list as important, while addressing criticisms often levelled at graph-theoretical approaches. We present results for three south-east Australian case-studies using continuous-value ecological condition surfaces as input. However, a simple habitat/non-habitat binary surface approximating a threshold ecological condition can also be used. GLCM has been designed to specifically address the need for generic landscape connectivity assessment at regional scales, and broader. It incorporates connectivity analyses across a range of spatial scales and granularities relevant to broad ranges of taxa and movement processes (foraging, dispersal and migration). Successively finer spatial scales are more intensively sampled based on a simple scaling-law. This approach allows analysis resolutions to be determined by data-driven ecological relevance rather than by processing limitations. The operational advantages of GLCM means that landscape connectivity assessments can be readily updated with refined or changed inputs including time-series remote sensing of land cover, or applied to alternative scenarios of land use, ecological restoration, climate projections or combinations of these.

An Innovative Framework on Spatial Boundary Optimization of Multiple International Designated Land Use

The continuous improvement of international protection awareness has dramatically increased the number of protection organizations and promoted various reserve-naming methods. However, the existing global natural reserves have either fully or partially overlapped, thereby allowing the same region to hold various international titles, resulting in serious issues, which are especially manifested in the boundary delimitation process of natural reserves. Therefore, delimiting the titles of reserve borders will become an enormous challenge in protected-area governance worldwide. This study conducted an in-depth investigation of the technical methods for delineating the spatial boundaries of natural reserves. Taking Jiangshan Nature Reserve in China as the case object, the Candidate Area–Natural background–Heritage Resource–Construction (C-NHC) framework was constructed, and the boundaries of the new reserves were delineated. This study has changed the status quo of the spatial overlap of the reserve through the quantitative evaluation of the conflict patches and the triple optimization of the boundary of the reserve. The area of the new reserve is 150.524 km2, which is 6.682 km2 larger than the original one. The original reserves are all included within the scope of the new one. This study provides guidance and new insights into the boundary delineation of integrated nature reserves worldwide.

Ready or not? Constructing the Monetary Union Readiness Index

Abstract While all EU Member States can join the group's monetary union, the euro area, some members are far more ready for the adoption and use of the single European currency. Here, we construct a new Monetary Union Readiness Index (MURI) for the EU Member States. The theoretical framework of the index is built on the economic theory of Optimal Currency Areas and EU regulations such as the Treaty and the Maastricht criteria, and the Regulation on the Macroeconomic Imbalance Procedure. The index measures (i) nominal convergence, (ii) real convergence, and (iii) macroeconomic stability. The MURI Index provides an easy to use real-time policy tool to evaluate both candidate and current euro area members. Hence, it complements, aggregates and communicates key information in annual convergence reports and in official statistics. Our evaluation finds that Austria, Finland, Denmark, Sweden and Germany showed the highest level of compliance with the different euro area criteria in 2018, while Greece, Cyprus, Romania, Spain, and Italy were the least compliant.

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network.

An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people’s attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people’s attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.

An efficient fire detection algorithm based on multi‐scale convolutional neural network

SummaryVideo fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is the best time for firefighting, it's crucial to develop a robust algorithm for early warning. In this paper, an efficient VFD fusion algorithm is presented. First, the fire candidate areas (FCA) are located quickly based on low‐level visual features to guarantee well timeliness. Furthermore, a multi‐scale convolutional neural network with spatial pyramid pooling is built and trained on the dedicated flame data set without the requirement of sample labeling. In this case, FCA with different aspect ratios and scales can be accurately identified. The method is fully tested on various databases of arbitrary‐sized images and videos. Experimental results show that the proposed fusion algorithm, in addition to improving the detection efficiency, also ensures the accurate identification of flames on different scales.

Spatial scenarios of potential electric vehicle adopters in Ireland

Transition to electric vehicles (EVs) is among public policy measures to reduce carbon emissions from the transport sector in Ireland. While EV adoption rates are increasing there is broad scepticism about achieving ambitious national policy targets. We employ microdata on commuting behaviour and different assumptions on the profile of adopters and purpose of vehicle use, based on existing literature, to identify clusters of candidates for transition to EV in Ireland. We estimate that depending on the assumption 17% to 42% of vehicle owners could comfortably switch to EV. High density areas of potential candidates for transition to EVs are identified in specific urban areas such as Cork and Dublin cities. We also find between 2 to 37% reduction in emissions from car owners based on the different set of assumptions we employ. While the per unit emission reduction in rural areas is higher, the aggregate emission reduction that can be achieved is higher in urban areas because of the higher density of candidates for transition in such areas as per our analysis. We show that apart from Dublin, urban areas are lacking when it comes to density of charging infrastructure.

A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite.

Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.

An innovative workflow for selecting appraisal area in low permeability greenfield development under uncertainties

There are uncertainties in both inherent geological properties and IOR/EOR performance parameters of low permeability greenfield reservoirs. Therefore, efforts to reduce uncertainties in the appraisal phase are necessary for the development and production phases. An adequate selection of the appraisal area in the hydrocarbon field is an imperative factor since the results of the appraisal well drilling and IOR/EOR pilot tests will be utilized for the development of the entire field. The major challenge in selecting an appraisal area is the lack of an integrated and systematic approach. In this study, we present a novel systematic and quantitative approach consisting of a better representative of reservoir features. To do this, some sort of cluster analysis over multiple realization is utilized. The underlying field of study is firstly segmented into appraisal candidate areas. Afterwards, the corresponding covariance matrix and recovery factor arrays are calculated for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered by fuzzy clustering method to choose the dominant cluster. Subsequently, the distances between each area and the center of dominant clusters could be used as a criterion for decision making. Moreover, the coefficient of variation for the oil volume changes is another criterion. Finally, the Shannon entropy weighting along with Reference Ideal Method (RIM)-Multi Attribute Range Evaluations (MARE) are applied. They are used as a multi-criteria decision-making method to calculate the Probabilistic Appraisal Opportunity Index (PAOI) in each area. The proposed method was applied for an appraisal study on an oil field in southwest Iran and, the optimum appraisal area is thereby determined based on the PAOI.

Thermochronologic perspectives on the deep-time evolution of the deep biosphere

The Earth's deep biosphere hosts some of its most ancient chemolithotrophic lineages. The history of habitation in this environment is thus of interest for understanding the origin and evolution of life. The oldest rocks on Earth, formed about 4 billion years ago, are in continental cratons that have experienced complex histories due to burial and exhumation. Isolated fracture-hosted fluids in these cratons may have residence times older than a billion years, but understanding the history of their microbial communities requires assessing the evolution of habitable conditions. Here, we present a thermochronological perspective on the habitability of Precambrian cratons through time. We show that rocks now in the upper few kilometers of cratons have been uninhabitable (>∼122 °C) for most of their lifetime or have experienced high-temperature episodes, such that the longest record of habitability does not stretch much beyond a billion years. In several cratons, habitable conditions date back only 50 to 300 million years, in agreement with dated biosignatures. The thermochronologic approach outlined here provides context for prospecting and interpreting the little-explored geologic record of the deep biosphere of Earth's cratons, when and where microbial communities may have thrived, and candidate areas for the oldest records of chemolithotrophic microbes.

Identifying the Development Potential for Urban Integration Based on Transport Accessibility—The Case for Northeast China

Urban integration, a new concept in urban and regional research, is an inevitable outcome of urban development. Based on accessibility analysis, this study uses urban and road network data to identify urban pairs in Northeast China which meet the distance threshold for urban integration and establishes an index system to evaluate the development potential. The research shows that in Northeast China, the results for urban integration are similar to those for the present urban structure in China, that is, a spatial pattern with provincial capital cities at the core and an urban belt as the axis. According to the evaluation, areas with potential for urban integration may be classified into four categories, namely, dominant areas, key areas, ordinary areas, and alternative areas. The candidate and potential areas for urban integration tend to be concentrated near a provincial capital city or a regional development zone. The results show that the development potential for urban integration varies greatly among the different urban pairs and the main constraining factors are different for each location. Among the constraining factors, the commuting time, costs, and economic conditions for the citizens are the most important.

Detecting unknown dams from high-resolution remote sensing images: A deep learning and spatial analysis approach

The quality and integrity of available dam data are critical for broad efforts to produce fine-scale assessments of their basin cycle, water quality, and other environmental and ecological effects. This study proposes a dam identification method in broad areas, object identification based on remote sensing images, and geographical analysis. First, we extracted dam candidate regions from broad surface water raster data at a spatial resolution of 30 m. Second, we trained and adjusted the multi-target recognition models using the dam sample from Google images, scanning dam candidate regions and extracting highly confidential dam positions. Moreover, we analyzed the location characteristics of the dams and used three geographical constraints to reduce background region overestimation further. The proposed framework was tested across an area of 13 265 km2 (Aomori, Kanagawa, and Okinawa) and yielded promising results, which reduced the candidate areas to 13.43% of the total water area. We validate the framework results using the available high-resolution historical image series available on Google Earth. The framework recalled 112 dams at a rate of 91.06%, with a precision rate of 80%. We simultaneously identified 39 dams that were not recorded in the known datasets. Our results reveal that the overall framework is reliable for automatic and rapid dam detection with a foundation of open geographic products. The framework proposed in this paper is the new attempt to combine deep learning target detection technology and spatial analysis with dam identification in broad areas.

Multi-level feature fusion for fruit bearing branch keypoint detection

Automated orchard operation has been a firm goal of fruit farmers for a long time. Deep learning-based approaches have been widely used to improve the performance of fruit detection, branch pruning, production estimating and other agricultural operations. This paper proposes a novel method to detect keypoint on the branch, which enables branch pruning during fruit picking. Specifically, a top-down framework for bearing branch keypoint detection is developed. First, a candidate area is generated according to the fruit-growing position and the fruit stem keypoint detection, which provides an attention region for further keypoint detection. Second, a multi-level feature fusion network which combines features in the same spatial sizes (intra-level) and from different spatial sizes (inter-level) is proposed to detect keypoint within the candidate area. The network can learn the spatial and semantic information and model the relationship among bearing branch keypoints. In addition, this paper constructs a citrus bearing branch dataset, which contributes to comprehensively evaluating the proposed method. Evaluation metrics on the dataset indicate the proposed method reaches an AP of 77.4% and an accuracy score of 84.7% with smaller model size and lower computing power consumption, which significantly outperforms several state-of-the-art keypoint detection methods. This study provides the possibility and foundation for performing automatic branch pruning during fruit harvesting.

IOU-Guided Siamese Tracking

Target tracking is currently a hot research topic in machine vision. The traditional target tracking algorithm based on the generative model selects target features manually, which has a simple structure and fast running speed, but it cannot meet the requirements of algorithm accuracy in complex scenes. Compared with traditional algorithms, due to the good performance, the tracking method based on full convolutional network has become one of the important methods of target tracking. However, the RPN-based Siamese network lacks positional reliability when predicting the target area. Aiming at the low tracking accuracy of the RPN-based Siamese network, this paper proposes an improved framework model named IoU-guided SiamRPN (IG-SiamRPN). In the proposed IG-SiamRPN, the IoU-guided branch is first constructed and sample pairs are generated through data augmentation. Then, the Jittered RoI is constructed to train the network to realize the direct prediction of the localization confidence of the candidate area. Subsequently, a target selection method based on predicted IoU scores is proposed, which uses predicted IoU scores instead of classification scores to optimize the target decision strategy of the Siamese network. Finally, an optimization-based fine-tuning method for the Siamese network frame is proposed, which solves the problem of location degradation and improves the performance of the algorithm. Compared with other state-of-the-art target tracking algorithms, experimental results on popular databases demonstrate that the proposed IG-SiamRPN can achieve better performance in both tracking accuracy and robustness.