Explore the vast range of titles available.

Flexible modeling frameworks for species distribution models based on generalized additive models that allow for smooth, nonlinear effects and interactions are of increasing importance in ecology. Commonly, the flexibility of such smooth function estimates is controlled by means of penalized estimation procedures. However, the actual shape remains unspecified. In many applications, this is not desirable as researchers have a priori assumptions on the shape of the estimated effects, with monotonicity being the most important. Here we demonstrate how monotonicity constraints can be incorporated in a recently proposed flexible framework for species distribution models. Our proposal allows monotonicity constraints to be imposed on smooth effects and on ordinal, categorical variables using an additional asymmetric L 2 penalty. Model estimation and variable selection for Red Kite ( Milvus milvus ) breeding was conducted using the flexible boosting framework implemented in R package mboost .

Most species of migrating birds use a combination of innate vector-based orientation programs and social information to facilitate accurate navigation during their life. A number of various interspecies hybridisations have been reported in birds. The traits of parents are expressed in hybrids in typical ways which are either intermediate, combined or heterotic. Here, we analyse the different migration behaviours of medium-sized raptors, i.e., Red Kites Milvus milvus , Black Kites Milvus migrans , and their hybrids. We chose six well-established parameters to compare the behaviour of Kite hybrids with those of both parental species. When comparing 16 quantified behavioural characteristics between Red Kites and F1 hybrids and between Black Kites and F1 hybrids, significant differences were found in 10 characteristics between Red Kites and F1 hybrids but only one of the 16 characteristics between Black Kites and F1 hybrids. Hence, F1 hybrid individuals showed behaviour much more similar to Black Kites than Red Kites. It implies that the basis of the migratory behaviour of Kites is an innate program with the dominance of genetic determinants supplemented by the use of social learning from individuals of the parent species.

Large, long-lived species with slow life histories and protracted pre-breeding stages are particularly susceptible to declines and extinction, often for unknown causes. Here, we show how demographic modeling of a medium-sized raptor, the Red Kite Milvus milvus, can aid to refocus conservation research and attention on the most likely mechanisms driving its decline. Red Kites’ survival and reproduction increased through three sequential stages for 1–2, 3–6, and 7–30 yr of age, mainly corresponding to individuals that are dispersing, attempting to gain a territory, and breeding. As typical of long-lived species, elasticities were highest for adult (≥7 yr old) survival, but this was high, with little scope for improvement. Instead, the declines were driven by an extremely low survival of pre-adults in their first years of life, which weakened the whole demographic system by nullifying the offspring contribution of adults and curtailing their replacement by recruits. For example, 27 pairs were necessary to generate a single prime age adult. Simulation of management scenarios suggested that the decline could be halted most parsimoniously by increasing pre-adult survival to the mean levels recorded for other areas, while only the synergistic, simultaneous improvement of breeding success, adult and pre-adult survival could generate a recovery. We propose three actions to attain such goals through selective supplementary feeding of both breeding and non-breeding individuals, and through mortality improvement by GPS remote-sensing devices employed as surveillance monitoring tools. Our results show how improving demographic models by using real, local vital rates rather than “best guess” vital rates can dramatically improve model realism by refocusing attention on the actual stages and mortality causes in need of manipulation, thus building precious time and resources for conservation management. These results also highlight the frequent key role of pre-adult survival for the management of long-lived species, coherent with the idea of demographic systems as integrated chains only as strong as their weakest link.

Background Periodontics and gingivitis are two of the most widely prevalent illnesses that affect people nowadays. The sixth most common disease in the world is periodontitis, and detecting periodontal bone loss is essential in the earlier condition and is crucial for the development of the proper diagnosis. Early bone loss detection can be assisted by using computer-assisted radiography examination. Understanding disease progression helps to select the most effective treatment action. Objectives An effective deep model is suggested to detect periodontal bone loss at an earlier stage for preventing the progression of Periodontics bone loss. Methods This work is intimated by collecting images from online resources. Further, the images gathered from the dataset are preceded by the tooth segmentation which is done using DenseUNet +  + . Further, the segmented images are given to the Adaptive DenseNet with Gated Recurrent Unit (AD-GRU) for detecting periodontal bone loss and this diagnosis is used for the periodontitis stage, where the ADGRU performance is augmented by optimizing the attributes using the Refined Red Kite Optimization Algorithm (RRKOA). Results The offered approach attained an accuracy of 94.45% which is higher than the88.63%, 90.58%, 89.54%, and 92.96% attained by the LSTM, DenseNet, GRU, DenseNet-GRU. Data conclusion The findings of the simulation proved the designed framework outperformed the traditional model with high accuracy. Clinical relevance The developed effectual deep model-based periodontal bone loss and stage periodontitis diagnosis structure is used in healthcare applications.

Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.Article highlightsIn this research work, a novel deep learning (DL)-based predictor model is attempted to be developed for charging electric vehicles.To suggests a hybrid prediction model that is built on an upgraded gate recurrent unit and an optimised convolution kernel of a fusion graph convolutional network (GCN).Red Kite Optimisation Algorithm (RKOA) selects the convolution kernel of the GCN optimally. The outcomes demonstrate the effectiveness of the prediction metrics calculated using the suggested neural predictor for the examined dataset when compared to earlier methods from published studies.

Attributes of natal habitat often affect early stages of natal dispersal. Thus, environmental gradients at mountain slopes are expected to result in gradients of dispersal behavior and to drive elevational differences in dispersal distances and settlement behavior. However, covariation of environmental factors across elevational gradients complicates the identification of mechanisms underlying the elevational patterns in dispersal behavior. Assuming a decreasing food availability with elevation, we conducted a food supplementation experiment of red kite (Milvus milvus) broods across an elevational gradient toward the upper range margin and we GPS‐tagged nestlings to assess their start of dispersal. While considering timing of breeding and breeding density across elevation, this allowed disentangling effects of elevational food gradients from co‐varying environmental gradients on the age at departure from the natal home range. We found an effect of food supplementation on age at departure, but no elevational gradient in the effect of food supplementation. Similarly, we found an effect of breeding density on departure age without an underlying elevational gradient. Supplementary‐fed juveniles and females in high breeding densities departed at younger age than control juveniles and males in low breeding densities. We only found an elevational gradient in the timing of breeding. Late hatched juveniles, and thus individuals at high elevation, departed at earlier age compared to early hatched juveniles. We conclude that favorable natal food conditions, allow for a young departure age of juvenile red kites. We show that the elevational delay in breeding is compensated by premature departure resulting in an elevational gradient in departure age. Thus, elevational differences in dispersal behaviour likely arise due to climatic factors affecting timing of breeding. However, the results also suggest that spatial differences in food availability and breeding density affect dispersal behavior and that their large‐scale gradients within the distributional range might result in differential natal dispersal patterns. By conducting a food supplementation experiment, we aimed at disentangling food availability and co‐varying elevational gradients affecting the onset of dispersal in juvenile red kites (Milvus milvus) along an elevational gradient toward the upper range margin. We found an effect of food supplementation and of breeding density on age at departure, as well as an elevational gradient in timing of breeding. We show that the elevational delay in breeding is compensated by premature departure resulting in an elevational gradient in departure age and therefore conclude that elevational differences in dispersal behavior likely arise due to climatic factors affecting timing of breeding.

The juvenile dispersal of raptors is a crucial stage that stretches from parental independence to the establishment of the first breeding area. Between 2012 and 2020, 44 juvenile red kites Milvus milvus from the Spanish breeding population were tagged using GPS telemetry to study their dispersal. Juveniles left the parental breeding area at the end of their first summer and performed wandering movements throughout the Iberian Peninsula, returning to the parental breeding area the following year, repeating the same pattern until they settled in their first breeding area. We analyzed the mean distance from the nest, the maximum reached distances, and the travelled distances (daily and hourly) during the first two years of dispersal and compared them. Despite the high individual variability, variables describing the dispersal movements of juveniles showed a decreasing trend during the second dispersal year: 80 % of individuals reached a shorter maximum distance in the second year, 70% decreased their mean distance to the nest, 65% decreased their hourly travelled distances, and 50% decreased their daily travelled distances. On the other hand, the Red Kites usually combined wandering movements with establishment of temporary settlement areas (TSA). The average duration of settlement in the TSAs was 75 ± 40 days (up to 182 days) and were located at 182 ± 168 km from the nest. In those areas, juveniles used 781.0 ± 1895.0 km 2 (KDE 95%). Some of the TSAs were used by several individuals, which suggests that these areas might be good targets for conservation in future management plans.

The high penetration of renewable energy resources’ (RESs) and electric vehicles’ (EVs) demands to power systems can stress the network reliability due to their stochastic natures. This can reduce the power quality in addition to increasing the network power losses and voltage deviations. This problem can be solved by allocating RESs and EV fast charging stations (FCSs) in suitable locations on the grid. So, this paper proposes a new approach using the red kite optimization algorithm (ROA) for integrating RESs and FCSs to the distribution network through identifying their best sizes and locations. The fitness functions considered in this work are: reducing the network loss and minimizing the voltage violation for 24 h. Moreover, a new version of the multi-objective red kite optimization algorithm (MOROA) is proposed to achieve both considered fitness functions. The study is performed on two standard distribution networks of IEEE-33 bus and IEEE-69 bus. The proposed ROA is compared to dung beetle optimizer (DBO), African vultures optimization algorithm (AVOA), bald eagle search (BES) algorithm, bonobo optimizer (BO), grey wolf optimizer (GWO), multi-objective multi-verse optimizer (MOMVO), multi-objective grey wolf optimizer (MOGWO), and multi-objective artificial hummingbird algorithm (MOAHA). For the IEEE-33 bus network, the proposed ROA succeeded in reducing the power loss and voltage deviation by 58.24% and 90.47%, respectively, while in the IEEE-69 bus it minimized the power loss and voltage deviation by 68.39% and 93.22%, respectively. The fetched results proved the competence and robustness of the proposed ROA in solving the problem of integrating RESs and FCSs to the electrical networks.

In Europe, bromadiolone, an anticoagulant rodenticide authorized for plant protection, may be applied intensively in fields to control rodents. The high level of poisoning of wildlife that follows such treatments over large areas has been frequently reported. In France, bromadiolone has been used to control water voles (Arvicola terrestris) since the 1980s. Both regulation and practices of rodent control have evolved during the last 15 years to restrict the quantity of poisoned bait used by farmers. This has led to a drastic reduction of the number of cases of poisoned wildlife reported by the French surveillance network SAGIR. During the autumn and winter 2011, favorable weather conditions and high vole densities led to the staging of several hundreds of Red Kites (Milvus milvus) in the Puy‐de‐Dôme department (central France). At the same time, intensive treatments with bromadiolone were performed in this area. Although no misuse has been mentioned by the authorities following controls, 28 Red Kites and 16 Common Buzzards (Buteo buteo) were found dead during surveys in November and December 2011. For all these birds, poisoning by bromadiolone as the main cause of death was either confirmed or highly suspected. Other observations suggest a possible impact of bromadiolone on the breeding population of Red Kites in this area during the spring 2011. French regulation of vole control for plant protection is currently under revision, and we believe this event calls for more sustainable management of rodent outbreaks. Based on large‐scale experiments undertaken in eastern France, we propose that direct control of voles at low density (with trapping or limited chemical treatments) and mechanical destruction of vole tunnels, mole control, landscape management, and predator fostering be included in future regulation because such practices could help resolve conservation and agricultural issues. Envenenamiento No Intencional de Fauna Silvestre y Propuestas para un Manejo Sustentable de Roedores.

Phishing attacks are now regarded as one of the most prevalent cyberattacks that often compromise the security of different communication and internet networks. Phishing websites are created with the goal of generating cyber threats in order to ascertain the user's financial information. Fake websites are frequently created and circulated online, which results in the loss of essential user assets. Phishing websites can result in monetary loss, intellectual property theft, damage to one's reputation, and disruption of regular business activities. Over the past decade, a number of anti-phishing tactics have been proposed to detect and reduce these attempts. They are still imprecise and ineffective, though. Deep Learning (DL), which can precisely learn the intrinsic features of the websites and recognize phishing websites, is one of the innovative techniques utilized to solve this issue. In this study, we proposed a novel OptSHQCNN phishing detection method. Pre-deployment and post-deployment are the two phases of the proposed methodology. The dataset undergoes preprocessing in the pre-deployment phase, which includes data balancing, and handling invalid features, irrelevant features, and missing values. The convolutional block attention module (CBAM) then extracts the main characteristics from web page code and linkages. The red kite optimization algorithm (RKOA) selects the significant key attributes in the third stage. The final phase involves classifying the data using the Shallow hybrid quantum-classical convolutional neural network (SHQCNN) model. To improve the effectiveness of the classification approach, the hyperparameters present in the SHQCNN model are fine-tuned using the shuffled shepherd optimization algorithm (SSOA). In the post-deployment phase, the URL is encoded using Optimized Bidirectional Encoder Representations from Transformers (OptBERT), after which the features are extracted. The retrieved properties are fed into a trained classifier. Next, a prediction of \"phishing\" or \"Legitimate\" is produced by the classifier. With a maximum of above 99% accuracy, precision, recall, and F1-score, respectively, the investigation's findings showed that the suggested technique performed better than other popular phishing detection methods. The creation of a security plugin for clients, browsers, and other instant messaging applications that operate on network edges, PCs, smartphones, and other personal terminals can be aided by these findings.