Sensor networks security based on sensitive robots agents. A conceptual model

Multi-agent systems are currently applied to solve complex problems. The security of networks is an eloquent example of a complex and difficult problem. A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion Detection is introduced in the current paper. The proposed technique could be used with machine learning based intrusion detection techniques. The new model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page

Population status of chimpanzees in the Masito-Ugalla Ecosystem, Tanzania.

More than 75 percent of Tanzania's chimpanzees live at low densities on land outside national parks. Chimpanzees are one of the key conservation targets in the region and long-term monitoring of these populations is essential for assessing the overall status of ecosystem health and the success of implemented conservation strategies. We aimed to assess change in chimpanzee density within the Masito-Ugalla Ecosystem (MUE) by comparing results of re-walking the same line transects in 2007 and 2014. We further used published remote sensing data derived from Landsat satellites to assess forest cover change within a 5 km buffer of these transects over that same period. We detected no statistically significant decline in chimpanzee density across the surveyed areas of MUE between 2007 and 2014, although the overall mean density of chimpanzees declined from 0.09 individuals/km(2) in 2007 to 0.05 individuals/km(2) in 2014. Whether this change is biologically meaningful cannot be determined due to small sample sizes and large, entirely overlapping error margins. It is therefore possible that the MUE chimpanzee population has been stable over this period and indeed in some areas (Issa Valley, Mkanga, Kamkulu) even showed an increase in chimpanzee density. Variation in chimpanzee habitat preference for ranging or nesting could explain variation in density at some of the survey sites between 2007 and 2014. We also found a relationship between increasing habitat loss and lower mean chimpanzee density. Future surveys will need to ensure a larger sample size, broader geographic effort, and random survey design, to more precisely determine trends in MUE chimpanzee density and population size over time. Am. J. Primatol. © 2015 Wiley Periodicals, Inc

Assessment of Chimpanzee Nests Detectability on Drone-Acquired Images

As with other species of great apes, chimpanzee numbers have declined during the past decades. Proper conservation of the remaining chimpanzees requires accurate and frequent data on their distribution and density. In Tanzania, 75% of the chimpanzees live at low densities on land outside national parks and little is known about their distribution, density, behavior or ecology. Given the sheer scale of chimpanzee distribution across western Tanzania (>20,000 km2), we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. Scientists have recently demonstrated the usefulness of drones to detect wildlife, including apes. Whilst direct observation of chimpanzees is unlikely given their elusiveness, we investigated the potential of drones to detect chimpanzee nests in the Issa valley, western Tanzania. Between 2015 and 2016, we tested and compared the capabilities of two fixed-wing drones. We surveyed twenty-two plots (50x500m) in gallery forests and miombo woodlands to compare nest observations from the ground with those from the air. We performed mixed-effects logistic regression models to evaluate the impact of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests spotted from the ground were detected from the air. From the factors tested, only image resolution significantly influenced nest detectability on drone-acquired images. We discuss the potential, but also the limitations of this technology for determining chimpanzee distribution and density and provide guidance for future investigation on the use of drones for ape population surveys. Combining traditional and novel technological methods of surveying allows more accurate collection on animal distribution and habitat connectivity that has important implications for apes conservation in an increasingly anthropogenically disturbed landscape

Spatio-temporal changes in chimpanzee density and abundance in the Greater Mahale Ecosystem, Tanzania

Authors would like to acknowledge the Arcus Foundation, Jane Goodall Institute, United States Agency for International Development (USAID), National Aeronautics and Space Administration (NASA), The Nature Conservancy, and Frankfurt Zoological Society for supporting, facilitating, and funding this work.Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species' range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the Endangered eastern chimpanzee (Pan troglodytes schweinfurthii), about 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio-temporal chimpanzee density and abundance across the GME, we employed density surface modelling, fitting a generalised additive model to a ten-year time series data set of nest counts based on line transect surveys. Chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (hereafter forests), 2.05% in miombo-woodlands (hereafter woodlands) and 3.45% in non-forests. These population declines were accompanied by ecosystem-wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for non-forests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km2, 95% CI 0.60-1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12-0.30) and non-forests (0.18, 95% CI 0.10-1.33). Although forests represent only 6% of the landscape, they support nearly a quarter of the chimpanzee population (769 chimpanzees, 95% CI 536-1,103). Woodlands dominate the landscape (71%) and thus support more than a half of the chimpanzee population (2,294; 95% CI 1,420-3,707). The remaining quarter of the landscape is represented by non-forests and supports another quarter of the chimpanzee population (750; 95% CI 408-1,381). Given the pressures on the remaining suitable habitat in Tanzania and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programmes implement a systematic random design to effectively inform and allocate conservation actions and facilitate inter-annual comparisons for trend-monitoring, measuring conservation success and guiding adaptive management.Publisher PDFPeer reviewe

Using Drones to Determine Chimpanzee Absences at the Edge of Their Distribution in Western Tanzania

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m2 plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images

Barriers to chimpanzee gene flow at the south-east edge of their distribution

Populations on the edge of a species' distribution may represent an important source of adaptive diversity, yet these populations tend to be more fragmented and are more likely to be geographically isolated. Lack of genetic exchanges between such populations, due to barriers to animal movement, can not only compromise adaptive potential but also lead to the fixation of deleterious alleles. The south-eastern edge of chimpanzee distribution is particularly fragmented, and conflicting hypotheses have been proposed about population connectivity and viability. To address this uncertainty, we generated both mitochondrial and MiSeq-based microsatellite genotypes for 290 individuals ranging across western Tanzania. While shared mitochondrial haplotypes confirmed historical gene flow, our microsatellite analyses revealed two distinct clusters, suggesting two populations currently isolated from one another. However, we found evidence of high levels of gene flow maintained within each of these clusters, one of which covers an 18,000 km2 ecosystem. Landscape genetic analyses confirmed the presence of barriers to gene flow with rivers and bare habitats highly restricting chimpanzee movement. Our study demonstrates how advances in sequencing technologies, combined with the development of landscape genetics approaches, can resolve ambiguities in the genetic history of critical populations and better inform conservation efforts of endangered species

Strategies, methods and tools for managing nanorisks in construction

This paper presents a general overview of the work carried out by European project SCAFFOLD (GA 280535) during its 30 months of life, with special emphasis on risk management component. The research conducted by SCAFFOLD is focused on the European construction sector and considers 5 types of nanomaterials (TiO2, SiO2, carbon nanofibres, cellulose nanofibers and nanoclays), 6 construction applications (Depollutant mortars, selfcompacting concretes, coatings, self-cleaning coatings, fire resistant panels and insulation materials) and 26 exposure scenarios, including lab, pilot and industrial scales. The document focuses on the structure, content and operation modes of the Risk Management Toolkit developed by the project to facilitate the implementation of "nano-management" in construction companies. The tool deploys and integrated approach OHSAS 18001 - ISO 31000 and is currently being validated on 5 industrial case studies.Research carried out by project SCAFFOLD was made possible thanks to funding from the European Commission, through the Seventh Framework Programme (GA 280535