Estimating moose population parameters from aerial surveys

Successful moose management depends on knowledge of population dynamics. The principal parameters required are size, rate of change, recruitment, sex composition, and mortality. Moose management in Alaska has been severely hampered by the lack of good estimates of these parameters, and unfortunately, this lack contributed to the decline of many Alaskan moose populations during the 1970s (e.g., Gasaway et al. 1983). The problems were: (1) population size not adequately estimated, (2) rapid rates of decline not acknowledged until populations were low, (3) meaningful recruitment rates were not available in the absence of good population estimates, and (4) calf and adult mortality rates were grossly underestimated. Frustration of moose managers working with inadequate data led to development of aerial survey procedures that yield minimally biased, sufficiently precise estimates of population parameters for most Alaskan moose management and research. This manual describes these procedures. Development of these procedures would have been impossible without the inspiration, support, advice, and criticism of many colleagues. We thank these colleagues for their contributions. Dale Haggstrom and Dave Kelleyhouse helped develop flight patterns, tested and improved early sampling designs, and as moose managers, put these procedures into routine use. Pilots Bill Lentsch and Pete Haggland were instrumental in developing and testing aerial surveying techniques. Their interest and dedication to improving moose management made them valuable allies. Statisticians Dana Thomas of the University of Alaska and W. Scott Overton of Oregon State University provided advice on variance approximations for the population estimator. Warren Ballard, Sterling Miller, SuzAnne Miller, Doug Larsen, and Wayne Kale tested procedures and provided valuable criticisms and suggestions. Jim Raymond initially programmed a portable calculator to make lengthy calculation simple, fast, and error-free. Angie Babcock, Lisa Ingalls, Vicky Leffingwell, and Laura McManus patiently typed several versions of this manual. John Coady and Oliver Burris provided continuous moral and financial support for a 3-year project that lasted 6 years. Joan Barnett, Rodney Boetje, Steven Peterson, and Wayne Regelin of the Alaska Department of Fish and Game provided helpful editorial suggestions in previous drafts. Finally, we thank referees David Anderson of the Utah Cooperative Wildlife Research Unit, Vincent Schultz of Washington State University, and James Peek, E. "Oz" Garton, and Mike Samuel of the University of Idaho whose comments and suggestions improved this manual. This project was funded by the Alaska Department of Fish and Game through Federal Aid in Wildlife Restoration Projects W-17-9 through W-22-1

DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin

Thermodynamic Studies of [H_(2)Rh(diphosphine)_2]^+ and [HRh(diphosphine)_(2)(CH_(3)CN)]^(2+) Complexes in Acetonitrile

Thermodynamic studies of a series of [H_(2)Rh(PP)_2]^+ and [HRh(PP)_(2)(CH_(3)CN)]^(2+) complexes have been carried out in acetonitrile. Seven different diphosphine (PP) ligands were selected to allow variation of the electronic properties of the ligand substituents, the cone angles, and the natural bite angles (NBAs). Oxidative addition of H_2 to [Rh(PP)_2]^+ complexes is favored by diphosphine ligands with large NBAs, small cone angles, and electron donating substituents, with the NBA being the dominant factor. Large pK_a values for [HRh(PP)_(2)(CH_(3)CN)]^(2+) complexes are favored by small ligand cone angles, small NBAs, and electron donating substituents with the cone angles playing a major role. The hydride donor abilities of [H_(2)Rh(PP)_2]^+ complexes increase as the NBAs decrease, the cone angles decrease, and the electron donor abilities of the substituents increase. These results indicate that if solvent coordination is involved in hydride transfer or proton transfer reactions, the observed trends can be understood in terms of a combination of two different steric effects, NBAs and cone angles, and electron-donor effects of the ligand substituents

Self-Aggregation Algorithms for Autonomic Systems.

Abstract One of the today issues in software engineering is to find new effective ways to deal intelligently with the increasing complexity of distributed computing systems. In particular, one of the aspects under study in the field of autonomic computing concerns the way such systems can autonomously reach a configuration that allows the entire system to work in a more efficient and effective way. In this paper we investigate how it is possible to obtain selfaggregation of distributed components. We have used existing self-aggregation algorithms as a starting point, and, after an analysis phase, we have discovered some aspects that could be improved. Finally we have derived new algorithms that showed improved self-aggregating performances in most of the situations. This work has been done in cooperation with Prof. Elisabetta Di Nitto and Prof. Raffaela Mirandola in the context of the CASCADAS European project

Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards

Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time. We develop and release an approach for automated safeguards experimentation to reveal their response to common security threats and privacy risks. We perform thousands of automated experiments using popular commercial IoT safeguards when deployed in a large IoT testbed. Our results indicate not only that these devices may be ineffective in preventing risks, but also their cloud interactions and data collection operations may introduce privacy risks for the households that adopt them

Idiosyncratic Brain Activation Patterns Are Associated with Poor Social Comprehension in Autism

Autism spectrum disorder (ASD) features profound social deficits but neuroimaging studies have failed to find any consistent neural signature. Here we connect these two facts by showing that idiosyncratic patterns of brain activation are associated with social comprehension deficits. Human participants with ASD (N = 17) and controls (N = 20) freely watched a television situation comedy (sitcom) depicting seminaturalistic social interactions (“The Office”, NBC Universal) in the scanner. Intersubject correlations in the pattern of evoked brain activation were reduced in the ASD group—but this effect was driven entirely by five ASD subjects whose idiosyncratic responses were also internally unreliable. The idiosyncrasy of these five ASD subjects was not explained by detailed neuropsychological profile, eye movements, or data quality; however, they were specifically impaired in understanding the social motivations of characters in the sitcom. Brain activation patterns in the remaining ASD subjects were indistinguishable from those of control subjects using multiple multivariate approaches. Our findings link neurofunctional abnormalities evoked by seminaturalistic stimuli with a specific impairment in social comprehension, and highlight the need to conceive of ASD as a heterogeneous classification

Data volume reduction for imaging radar polarimetry

Two alternative methods are disclosed for digital reduction of synthetic aperture multipolarized radar data using scattering matrices, or using Stokes matrices, of four consecutive along-track pixels to produce averaged data for generating a synthetic polarization image