Early life movements and mortality of Egyptian vultures: Implications for transcontinental conservation.

Understanding the movements and mortality of individuals across different life stages is crucial for the effective conservation of wild populations. We used data from 32 Egyptian vultures (Neophron percnopterus) tagged with GPS transmitters as nestlings in three Iberian breeding areas to study their dependence period, migration routes, movements in Africa, and mortality at each stage. Our results show no significant differences in the timing of nest departure or the duration of the dependence period between individuals of different sexes or breeding nuclei. Most juveniles migrated to sub-Saharan Africa in their first year, but some (3 of 32, 9.4%) remained in the Iberian Peninsula. Individuals that migrated to Africa did so annually, while those remaining in Iberia never migrated to the Sahel, indicating distinct migratory and non-migratory strategies. Non-migratory individuals consistently moved northward during the breeding season to their natal territories. Siblings did not coordinate their migration strategy or timing. All juveniles showed extensive overlap in the vast areas used in Africa, where females arrived before males, and in the Iberian Peninsula. Our study also revealed that no juveniles died immediately after fledging, but that none of the tagged individuals lived more than 7 years or were recruited as breeders. Although most casualties occurred during the longer stay in the Sahel, the mortality rate was highest during the few days of the first migration. Our results show that despite small variations in movement patterns between breeding nuclei and sexes, Egyptian vultures face similar challenges during the years before recruitment as breeders, mostly determined by their migratory strategy. These findings are relevant for designing conservation strategies, both in breeding areas and, more importantly, in wintering areas and along migration pathways. Such strategies will significantly impact the entire Iberian population of this endangered species.

A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization.

INTRODUCTION: Big data optimization (Big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient. OBJECTIVES: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve Big-Opt problem. METHODS: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm; shift density estimation is introduced as a mechanism for providing selection pressure for population evolution; and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to Big-Opt problems. RESULTS: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms. CONCLUSION: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data.

Successful captive breeding of vultures due to the double clutching method.

After a massive decline, the Griffon Vulture (Gypsfulvus) population in Bulgaria has now stabilised as a result of the hard work of conservation programmes, although it is still listed as an Endangered species (EN) in the country's Red Data Book. Due to a series of unfortunate events, another species - the Cinereous Vulture (Aegypiusmonachus) became extinct in Bulgaria and recovered only recently due to systematic re-introduction efforts. Along with the poor living conditions, a predisposing factor for the decreasing population of the vultures is the fact they hatch only a single egg; two may be laid on exceptions. The survival rate of the young is from medium to low. In that order, a method of double clutching has been applied in the hope of preserving and retrieving both of the vultures. This article aims to introduce the double clutching method in detail, as well as to show the results of its implementation into the breeding programme of the Cinereous Vulture and the Griffon Vulture at the Wildlife Rehabilitation and Breeding Centre of Green Balkans - Stara Zagora NGO. This research studies the time period 2019-2023. The purpose of this study is to compare the results of natural copulation and hatching and those of the double clutching method. The data taken are used for studies for both the Cinereous and the Griffon Vulture's reproduction in correlation with their feeding programme and living conditions. The results of the study will be summarised into the following text for the purposes of shedding light on the positives of the double clutching method.

High conservation importance of range-edge populations of Hooded Vultures (Necrosyrtes monachus).

Critically endangered Hooded Vultures (Necrosyrtes monachus Temminck, 1823), like many vulture species globally, are experiencing rapid population declines due to anthropogenic factors such as poisonings, human persecution, trading for belief-based use, and habitat loss/degradation. The Hooded Vulture is widespread across sub-Saharan Africa. Although it is considered one of the most abundant vultures in West Africa, this vulture species is less common in East and southern Africa, with the population at the southern-most edge of the distribution (in South Africa and Eswatini) estimated at only 100-200 mature individuals. The distribution of Hooded Vultures has contracted dramatically in southern Africa, with breeding populations largely confined to protected areas such as the Greater Kruger National Park. This study aimed to investigate the genetic diversity of the southern African range-edge population and assess if the recent contraction in the distribution has resulted in the population experiencing a genetic bottleneck. Sixteen microsatellite loci were amplified for samples collected along the Olifants River in the Greater Kruger National Park (n = 30). The genetic diversity in the South African population was compared to samples (n = 30) collected in Ghana, where Hooded Vultures are more abundant. Contrary to expectations, the South African peripheral Hooded Vulture population showed higher levels of heterozygosity (HO = 0.495) than the Ghanaian population (HO = 0.315). Neither population showed signs of recent bottleneck events when tested using demographic modelling and Approximate Bayesian computation (ABC). However, both populations showed high levels of inbreeding and relatedness. Our results suggest that despite being a small peripheral population, the South African Hooded Vulture population showed a similar level of genetic diversity as individuals sampled from a core population within the species distribution (in Ghana). This study supports the need for Hooded Vulture conservation efforts in the southern African region and highlights the evolutionary importance of range-edge populations.

Novel optimized coupled rainfall model simulation based on stepwise decomposition technique.

Precipitation forecasting plays a pivotal role in guiding the effective management of regional water resources and providing crucial warnings for regional droughts and floods. Finding a monthly precipitation simulation model with robust fitting performance is a significant research endeavor in practical precipitation prediction. This paper introduces two modified African vulture optimization algorithms (MAVOA1 and MAVOA2). It provides hyperparameter optimization techniques for the least squares support vector machine (LSSVM), long short-term memory neural network (LSTM), and random forest (RF) models. These techniques are used to construct a monthly precipitation simulation model based on algorithmic optimization coupled with variational mode decomposition for full decomposition. The test results at five typical stations in the North China Plain reveal the following: (1) the LSSVM model demonstrates significantly better performance than the LSTM and RF models. (2) the MAVOA2-LSSVM model has the best-integrated effect: the average test fitting error is RMSE = 17.50 mm/month, MRE = 117.25%, NSE = 0.90, which shows its superiority in practical application and can significantly improve the accuracy of precipitation prediction; MAVOA2 is more suitable for machine learning models with more hyperparameters of its own, which provides a reference for hyperparameter optimization algorithms in the other fields.

Predicting the compressive strength of self-compacting concrete by developed African vulture optimization algorithm-Elman neural networks.

The compressive strength of concrete depends on various factors. Since these parameters can be in a relatively wide range, it is difficult for predicting the behavior of concrete. Therefore, to solve this problem, an advanced modeling is needed. The aim of the literature is to achieve an ideal and flexible solution for predicting the behavior of concrete. Therefore, it is necessary to develop new approaches. Artificial Neural Networks (ANNs) have evolved from a theoretical method to a widely utilized technology by successful applications for a variety of issues. Actually, ANNs are a strong computing tool that provides the right solutions to problems that are difficult to use conventional methods. Inspired by the biological neural system, these networks are now widely used for solving a wide range of complicated problems in civil engineering. This study''s target is evaluating the performance of developed African vulture optimization algorithm (DAVOA)-Elman neural networks (ENNs) by considering different input parameters in predicting the self-compacting concrete compressive strength. Hence, once 8 parameters and again to get as close as possible to the prediction conditions in the laboratory, 140 parameters entered to the improved version of Elman Neural Networks as input. According to the results, the element network has the lowest mean squares of the test error in predicting the compressive strength of 7 and 28 days in 100 repetitions. Further, in predicting both compressive strengths, the element grid with the Logsig-Purelin interlayer transfer function has the lowest test error, which determines the optimal transfer function. Moreover, the results showed that DAVOA as a reliable tool with time and cost savings have high power in predicting the desired characteristics. Also, in predicting both 7-day and 28-day compressive strength, networks built with 140 parameters have a 74.54 and 70.44% improvement in test error over 8-parameter networks, respectively, which directly affects this effect. Further parameters are considered as input to the network error rate in predicting the desired properties.

A novel optimization rainfall coupling model based on stepwise decomposition technique.

Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.

The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel's band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy; 14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.

Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer.

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries.

Spillover of Newcastle disease virus to Himalayan Griffon vulture: a possible food-based transmission.

The Newcastle disease virus (NDV) affects wild and domesticated bird species, including commercial poultry. Although the diversity of NDV in domestic chickens is well documented, limited information is available about Newcastle disease (ND) outbreaks in other bird species. We report an annotated sequence of NDV/Vulture/Borjuri/01/22, an avirulent strain of NDV reported from Borjuri, Northeast India, in Himalayan Griffon vulture. The complete genome is 15,186 bases long with a fusion protein (F) cleavage site 112GRQGR↓L117. The phylogenetic analysis based on the F protein gene and the whole genome sequence revealed that the isolate from the vulture belongs to genotype II, sharing significant homology with vaccine strain LaSota. The study highlights the possible spillover of the virus from domestic to wild species through the food chain.

RAPID POINT-OF-CARE TESTING FOR DETECTION OF ANTIBODIES TO TOXOPLASMA GONDII IN BLACK VULTURES AND RING-BILLED GULLS FROM PENNSYLVANIA.

Toxoplasma gondii is a zoonotic protozoan parasite that infects most warm-blooded animals, including birds. Scavenging birds are epidemiologically important hosts because they can serve as indicators of environmental T. gondii levels. A rapid point-of-care (POC) test that detects antibodies to T. gondii in humans is commercially available. In this research, we assessed the ability of the human POC test to detect anti-T. gondii antibodies in 106 black vultures (Coragyps atratus) and 23 ring-billed gulls (Larus delawarensis) from Pennsylvania, USA. Serum samples were tested with the POC test and compared to the modified agglutination test (MAT) in a blinded study. Overall, anti-T. gondii antibodies were detected in 2.8% (3/106) of black vultures and 60.9% (14/23) of ring-billed gulls by the POC test. One false-positive POC test occurred in a black vulture that was negative by MAT. False-negative results were obtained in 2 black vultures and 4 ring-billed gulls that had MAT titers of 1:25 or 1:50. The sensitivity and specificity of the POC for both black vultures and ring-billed gulls combined were 95.7% and 95.5%, respectively. This is the first study using human POC tests to detect antibodies to T. gondii in birds. Further study of the rapid test as a screening tool for serological surveillance of T. gondii in birds is warranted.

Hooded Vultures Necrosyrtes monachus are at risk of extinction in Benin: A result of poaching for belief-based use and decreasing food availability.

In recent years, Hooded Vulture populations in West Africa have decreased substantially. However, in some areas within this region, the species is still relatively abundant. To find out more about the situation in West Africa, we assessed the status of Hooded Vultures in Benin, one of the countries where their status is not well known. We conducted road counts on paved and unpaved roads and along small trails over a total of 1451 km. We also conducted interviews with local abattoir watchmen, veterinarians, butchers and foresters to examine potential threats to this species. A total of 52 Hooded Vultures were counted mostly in the departments of Atacora (32) but also in Alibori (10) and Borgou (10). The relative abundance was four Hooded Vultures per 100 km, highlighting the near extirpation of this bird from Benin. A total of 49 interviews revealed that poaching for belief-based use (through shooting and traps) and decreasing food availability remain the most important threats for Hooded Vultures in northern Benin. If these threats are not mitigated, we predict the extirpation of the Hooded Vulture outside protected areas in Benin within the next two decades, possibly even sooner. Conservation measures, including awareness campaigns, and the improvement and enforcement of environmental legislation, must be urgently implemented to improve the protection of this Critically Endangered vulture species.

A method of building information modelling implementation for structural engineering firms.

Building Information Modelling (BIM), a new concept and methodology, has received much attention lately. Various Structural Engineering Firms (SEFs) have observed substantial competitive benefits after its deployment. BIM offers a wide range of advantages, but its capacity has not even been completely exploited. The challenges of firm implementation, a procedure that necessitates significant alterations in firm business structure, are a major factor in this. However, there hasn't been much in-depth research on the assessment and integration of the research around the application of BIM in firms. To address the planning phase's complexity, which makes implementing BIM in these workplaces challenging, this article provides a framework for BIM deployment in the SEF. Additionally, a brand-new hybrid African Buffalo and African Vulture Optimization (AB-AVO) has been created to assess the state of technology. The technique suggested for BIM execution within SEF obviously and effectively recognises the firm's expectations and resources, sets out the needs required to create the BIM technique, and offers technical and clinical suggestions for monitoring and planning the execution. It is categorised by resource utilization, versatility, and adaptability.•This paper introduces a new concept and methodology of using BIM•The technique suggested for BIM execution within SEF obviously and effectively recognises the firm's expectations and resources•The method establishes guidelines for structural firm to adopt BIM in their monitoring, and planning the execution.

Blood lead levels in an endangered vulture decline following changes in hunting activity.

Lead ammunition stands out as one of the most pervasive pollutants affecting wildlife. Its impact on bird populations have spurred efforts for the phase-out of leaded gunshot in several countries, although with varying scopes and applications. Ongoing and future policy changes require data to assess the effectiveness of adopted measures, particularly in the current context of biodiversity loss. Here, we assessed the long-term changes in blood lead (Pb) levels of Egyptian vultures from the Canary Islands, Spain, which have been severely affected by Pb poisoning over the past two decades. During this period, the reduction in hunting pressure and changes in legislation regarding firearms usage for small game hunting likely contributed to a decrease in environmental Pb availability. As anticipated, our results show a reduction in Pb levels, especially after the ban on wild rabbit hunting with shotgun since 2010. This effect was stronger in the preadult fraction of the vulture population. However, we still observed elevated blood Pb levels above the background and clinical thresholds in 5.6% and 1.5% of individuals, respectively. Our results highlight the positive impact of reducing the availability of Pb from ammunition sources on individual health. Nonetheless, the continued use of Pb gunshot remains an important source of poisoning, even lethal, mainly affecting adult individuals. This poses a particular concern for long-lived birds, compounding by potential chronic effects associated with Pb bioaccumulation. Our findings align with recent studies indicating insufficient reductions in Pb levels among European birds of prey, attributed to limited policy changes and their uneven implementation. We anticipated further reductions in Pb levels among Egyptian vultures with expanded restrictions on hunting practices, including a blanket ban on Pb shot usage across all small game species.

The use of social information in vulture flight decisions.

Animals rely on a balance of personal and social information to decide when and where to move next in order to access a desired resource. The benefits from cueing on conspecifics to reduce uncertainty about resource availability can be rapidly overcome by the risks of within-group competition, often exacerbated toward low-ranked individuals. Being obligate soarers, relying on thermal updraughts to search for carcasses around which competition can be fierce, vultures represent ideal models to investigate the balance between personal and social information during foraging movements. Linking dominance hierarchy, social affinities and meteorological conditions to movement decisions of eight captive vultures, Gyps spp., released for free flights in natural soaring conditions, we found that they relied on social information (i.e. other vultures using/having used the thermals) to find the next thermal updraught, especially in unfavourable flight conditions. Low-ranked individuals were more likely to disregard social cues when deciding where to go next, possibly to minimize the competitive risk of social aggregation. These results exemplify the architecture of decision-making during flight in social birds. It suggests that the environmental context, the context of risk and the social system as a whole calibrate the balance between personal and social information use.

Solving bi-objective economic-emission load dispatch of diesel-wind-solar microgrid using African vulture optimization algorithm.

Microgrid is a localised power generation infrastructure designed to provide continuous and reliable power supply to a small, specific region. The increasing concern towards environmental sustainability has resulted in the prioritisation of non-emitting Renewable Energy Sources (RESs) while optimal sizing of microgrid. Optimal sizing of generation units at minimum cost with minimum emission satisfying various practical constraints is a challenging bi-objective optimization problem of power system known as Economic-Emission Load Dispatch (EELD). Metaheuristic approaches are predominantly used to solve the EELD problem. This article explores the advanced metaheuristic methods to solve EELD problem and proposes application of African Vulture Optimization Algorithm (AVOA) to subsequently address the EELD problem of a microgrid combining diesel, wind, and solar energy sources based on field data of a specific location in Jaisalmer, India. AVOA emulates the foraging and navigation patterns of vultures, incorporating effective exploration and exploitation characteristics. The effectiveness of AVOA is first validated using three standard test systems of 10, 6 (IEEE30-bus), and 40 units with/without transmission losses, prior applying it for microgrid. The obtained results are compared with several other popular optimization techniques to establish the efficacy of proposed method. Further, AVOA is employed to analyse the impact of individual RESs on microgrid's cost and emissions across three distinct generation scenarios. The viability score is employed to evaluate the efficacy of all techniques along with other significant performance indices. Statistical data tests such as ANOVA, Wilcoxon, and robustness are employed to assess the statistical confidence of the AVOA. Additionally, a multi-comparison post-hoc TukeyHSD test is introduced which proves the superiority of AVOA. Results establish AVOA as the most effective solution for addressing the EELD problem in microgrid (all sources), with significant reduction of 5.25% and 33.09% in cost (323318.21$/day) and emission (of 2433.95 Tons/day) respectively compared to the closest competitive method.

Vulture poisoning in Sub-Saharan Africa and its implications for conservation planning: A systematic review.

The Old World Vultures (OWV), constituting 16 species primarily in Africa, Europe and Asia, are currently being driven to extinction mostly by anthropogenic activities, especially poisoning. The vulture losses from poisoning caused by human-related activities are en masse at a single mortality event-level and occur in complex social-ecological systems. There has been a growing body of knowledge on wildlife poisoning over the years. However, no review has been done to consolidate vulture poisoning studies in sub-Saharan Africa (SSA), with a social lens of conservation planning. Here we present a review of the vulture poisoning research by re-contextualizing the problem of vulture poisoning across SSA. We employed stepwise Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to search for literature on vulture poisoning. The search yielded 211 studies which were trimmed to 55 after applying sets of eligibility criteria. Literature shows that efforts aimed at successful vulture conservation planning will require an understanding of the relational aspects of stakeholder social capital (assets) that are critical to the implementation of species recovery strategies. Strengthening relational social capital through multi-scale stakeholder evidence-based awareness creation and participation is necessary for addressing the African Vulture Crisis (AVC). Applying stakeholder social capital approaches to different vulture conservation scenarios at local, regional and international scales can enhance successful implementation of conservation strategies for the persistence of vultures in complex socio-ecological systems in African landscapes. Existing literature also showed the importance of stakeholder social capital as a countermeasure against vulture losses.

Forensic investigations of suspected livestock depredation by vultures: scientific tools for compensation programmes.

Human-wildlife conflicts may have devastating consequences for fauna due to targeting by humans of wildlife populations suspected to have predated livestock. Suspicion of depredation of extensively raised livestock by vultures in Europe has triggered public administration-led forensic investigations intended to distinguish between predation and scavenging in order to compensate farmers for attacks on their livestock. In this study, gross and histological analyses were carried out on suspected cases of domestic animal depredation by griffon vultures (Gyps fulvus) over a 1-year period. Fifty-eight animals were affected (n = 41, suspected depredation), including domestic cattle (75.9%), sheep (22.4%) and a goat (1.7%). All the adults affected were female and most cases of suspected depredation occurred during the peripartum period (56%). Histological investigations distinguished between post-mortem (84% of diagnostic samples) and ante-mortem (16% of diagnostic samples) cases, and gross examinations revealed significant differences (P ≤ 0.002) between ante-mortem, post-mortem and non-diagnostic samples. This study highlights the need to optimize sampling protocols to increase the success of forensic studies. The forensic investigations presented here may be applied to resolve human-wildlife conflicts involving not only vultures but other endangered carnivores and contribute to human-wildlife coexistence in rural areas by protecting the interests of both the livestock sector and endangered species.

Age-Specific Demographic Response of a Long-Lived Scavenger Species to Reduction of Organic Matter in a Landfill.

Food availability shapes demographic parameters and population dynamics. Certain species have adapted to predictable anthropogenic food resources like landfills. However, abrupt shifts in food availability can negatively impact such populations. While changes in survival are expected, the age-related effects remain poorly understood, particularly in long-lived scavenger species. We investigated the age-specific demographic response of a Griffon vulture (Gyps fulvus) population to a reduction in organic matter in a landfill and analyzed apparent survival and the probability of transience after initial capture using a Bayesian Cormack-Jolly-Seber model on data from 2012-2022. The proportion of transients among newly captured immatures and adults increased after the reduction in food. Juvenile apparent survival declined, increased in immature residents, and decreased in adult residents. These results suggest that there was a greater likelihood of permanent emigration due to intensified intraspecific competition following the reduction in food. Interestingly, resident immatures showed the opposite trend, suggesting the persistence of high-quality individuals despite the food scarcity. Although the reasons behind the reduced apparent survival of resident adults in the final four years of the study remain unclear, non-natural mortality potentially plays a part. In Europe landfill closure regulations are being implemented and pose a threat to avian scavenger populations, which underlines the need for research on food scarcity scenarios and proper conservation measures.

Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking.

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively.