Vulture Strike Download] [FULL]
Tabatha Pasqua
Members of the MacDill Air Force Base Bird/Wildlife Aircraft Strike Hazard (BASH) program, stand on the flightline while an A-10 Thunderbolt II aircraft takes off from MacDill AFB, Fla., Jan. 10, 2019. BASH, which aims to keep birds and wildlife from interfering with aircraft operations, conducted a study of vulture migration patterns to better understand their habits, and develop plans to prevent aircraft bird strikes. (U.S. Air Force photo by Senior Airman Adam R. Shanks)
Kory McLellan, a wildlife biologist with the United States Department of Agriculture, uses pyrotechnics to disperse birds away from the airfield, Feb. 16, 2016, at MacDill Air Force Base, Fla. The loud noise of the pyrotechnics provides an effective tool to disperse birds and prevent bird strikes. (U.S. Air Force photo by Airman 1st Class Mariette Adams)
Based on the four vultures tagged, three black vultures stayed around the South Tampa area, while a lone turkey vulture migrated to southern Ohio for the summer. Once winter approached, this turkey vulture returned to Florida, crossing MacDill once again.
After migrating, the turkey vultures fly back to Florida and tend to pass over MacDill in search of food. However, their search for food poses a huge risk to the KC-135 Stratotanker fleet especially during takeoffs and landings.
Aircraft collisions with wildlife cause millions of dollars in damage annually resulting in the loss of combat capability. At MacDill, from 1990-2018, there have been four strikes with turkey vultures causing more than $179,000 in damages.
Wildlife strike hazards to aircrew and aircraft, as well as operations and maintenance expenditures, may be significantly reduced by utilizing an integrated pest management approach, resulting in substantial savings of Air Force resources.
Collisions between wildlife and aircraft are a major safety concern for international aviation. In the Americas, vultures (Cathartidae) are considered to be one of the most hazardous bird species to airport operations. In this study, we evaluated the use of translocations as a management technique to reduce vulture abundance near the Manaus International Airport (MAO), Manaus, Brazil. The MAO is one of the busiest and most strategically important airports in South America, often referred to as the gateway to the Brazilian Amazon. We captured, wing-tagged, and translocated 98 vultures between August and October 2013 and between January and April 2014. The wing-tags were colored plastic tags specifically developed to tag vultures to enhance identification in flight and not alter bird behavior. The tagged vultures were translocated different distances (100, 150, and 200 km) from MAO. Only 25.5% of translocated vultures returned to the airport. However, the relative abundance of vultures did not differ between monitoring periods before and after captures and translocations. Our results demonstrated that the translocations failed to decrease MAO vulture abundance. We recommend habitat modifications associated with nonlethal (dispersion by bird repellents) and lethal (kill some individuals reinforcing dispersion) strategies to reduce vulture bird strike risks.
As part of a continuing study of black vultures in the area surrounding Shepherd Field in Martinsburg, W.Va., home of the 167th Airlift Wing and the Eastern West Virginia Regional Airport, five of the large scavenger birds were fitted with transmitting devices, August 18.
Since then, nearly 300 black vultures have been tagged in the area as part of the study which was prompted when the birds began roosting on Argos property in 2017. With Argos located just 2 miles from the airfield, black vultures flying in that vicinity pose a threat to local air traffic.
The information provided by the tracking devices and the reported sightings will be used to identify high risk areas and aid pilots in their risk assessments and hopefully avoid aircraft vulture strikes.
Anyone who spots a tagged black vulture in the area is asked to send an email to vultu...@gmail.com with the location of the sighting, a tag number if visible and any behavioral information, or go to
FND techniques have been getting extra attention since the circulation of disinformation has increased on the Internet, which has become a concern of the modern community [1]. Generally, the concept of FNs has been around for a while. This problem existed before the growth of the Internet. Many publishers utilize misinformation to promote their interests [2]. Many publishers publish FNs through convenient print media news and online platforms. Online platforms play an essential role in disseminating FNs in the community; these online platforms, such as online newspapers and social media, provide users access to various publications in one session to provide greater ease and speed than printed news media. In addition, the nature of social networks suggests an accessible platform for the fast dissemination of information in real-time; even with the reliability of this information, it has caused severe information credibility problems [3].
Not only do FNs negatively affect individuals, but they devastate the community as a whole over time. For example, FNs went viral on Facebook in the US 2016 presidential election instead of the more popular and trusted traditional news sources [4], revealing that readers may pay more attention to FNs than truthful news. Social media users who participate in spreading disinformation can have many motivations for spreading such information online, such as manipulation, political agendas, and influence. Still, while many of these users are genuine, those spreading disinformation may or may not be genuine users [5]. Because social media profiles are inexpensive and uncomplicated, many people have created social media profiles for malicious tasks. If a computer algorithm manages social media profiles, it will be used as a social bot [4]. These social bots can interact with individuals via social media and automatically produce and publish content online, making it significantly challenging for individuals to recognize such manipulated content [6].
[15] proposed a DL method based on an automated detector via a three-level hierarchical focus network for fast and accurate FND. [16] proposed deep Convolutional Neural Networks (CNNs) for detecting FNs. [14] presented a learning model based on linguistic features to detect FNs. [17] presented a method for FND using a hybrid neural network structure, integrating the power of Long Short-Term Memory (LSTM) and CNNs. [13] presented several attributes-oriented methods for the automated detection of FNs on social media employing DL. [18] presented three DL-based models intended to classify and detect FNs. [19] presented a method for FND employing a geometric DL. [20] introduced a neural network method to accurately forecast the stance between a given pair of headlines and the text of the article. [21] introduced several methods for FND based on the relationship between the headlines and the body of the articles. Their methods are primarily based on Bidirectional-LSTM, CNN, and LSTM.
Due to their effective performance in addressing many optimization problems, meta-heuristic algorithms have attracted much attention recently. Therefore, MHA is an efficient solution-finding method to detect FNs on social media. [22] introduced the issue of detecting FNs as an optimization problem. This study proposes two meta-heuristic algorithms, Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO), for tackling the FND issue. The proposed FND approach is initialized through a pre-processing phase and then utilizes GWO and SSO to handle the FND issue. The suggested approach was verified utilizing three real-world FNs datasets. The experimental outcomes show that the GWO optimization algorithm achieved optimal results in different performance metrics than the SSO optimization algorithm and other meta-heuristic algorithms. [23] improved their study by proposing a new method that integrated MHA and text mining to discover FNs via online social media. Modified variants of GWO and SWO optimization algorithms based on nonlinear decreasing coefficient and oscillating inertia weight are used for the FND issue. The evaluation measures of the suggested approaches are verified on different datasets. The empirical outcomes revealed that the proposed new approaches exceeded other approaches in real-world FNs datasets. [24] introduced a new method for identifying FNs articles using the WOA-Xgb-Tree technique and content-driven attributes. The suggested model can be implemented in several scenarios for classifying news articles. The proposed model has two phases: first, the necessary attributes are identified and investigated. Then, the Xgb-Tree optimizer tuned by the Whale Optimization Algorithm (WOA) classifies the news articles using the specified attributes. In their empirical results, They considered F1-score and classification accuracy as the basis of their investigations. Then, they compared the results of their proposed system to various modern classification techniques using a dataset that has collected more than 40,000 news articles recently. The empirical outcomes reveal that the suggested system obtained a reliable F1-score rate and efficiently classified more than 91 percent of the articles.
This paper presents a framework relying on the IBAVO-AO algorithm to tackle the issue of FND. The proposed IBAVO-AO is a hybrid AVO-AO optimizer with an Xgb-Tree classifier. The primary stages of the suggested methodology are as follows: Firstly, the collected unstructured data is converted into structured data for usage in the classification process, known as data pre-processing. In this stage, beneficial features are extracted by removing superfluous words and unnecessary special symbols, stemming from altering words into root words, tokenizing the resulting data into a bag of words, and finally encoding and padding words into sequence vectors of numerical values using Global Vectors (GLOVE) [25, 26], which is a count-based approach for pre-training and relies on terms or vectors from co-occurrence data. After that, the extracted features are filtered using an efficient Relief algorithm to determine only the associated features and provide the final classification dataset. Using the Relief algorithm aims to enhance the ability to explore the best outcomes discovered inside the solution space. In the final stage, the classification process utilizes the IBAVO-AO algorithm based on the Xgb-Tree classifier with high detection performance. The effectiveness of the suggested methodology is assessed by employing a variety of evaluation metrics and applying them to the ISOT-FNs dataset that includes more than 44 thousand news articles. After the suggested methodology has been evaluated and compared with state-of-the-art optimization techniques [27, 28], the results indicate that the presented methodology produces high classification accuracy. It is advised to use it in the FND problem.
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