The success of using trained dogs to locate sparse rodents in pest-free sanctuaries

2010 ◽  
Vol 37 (1) ◽  
pp. 39 ◽  
Author(s):  
Anna Gsell ◽  
John Innes ◽  
Pim de Monchy ◽  
Dianne Brunton
Keyword(s):  
Large Scale ◽  
Research Area ◽  
Detection Methods ◽  
Wild Rodents ◽  
Conservation Areas ◽  
Laboratory Rats ◽  
Scent Trails ◽  
The Cost ◽  
Rats And Mice ◽  
Norway Rats

Context. Better techniques to detect small numbers of mammalian pests such as rodents are required both to complete large-scale eradications in restoration areas and to detect invaders before they become abundant or cause serious impacts on biodiversity. Aims. To evaluate the ability of certified rodent dogs (Canis familiaris) to locate Norway rats (Rattus norvegicus) and mice (Mus musculus) or their scent trails at very low densities in field conditions. Methods. We experimentally tested two rodent dogs by releasing small numbers of laboratory rats and mice in a 63 ha rodent-free forest sanctuary and then determining if the dogs and their handlers could find the rodents and their scent trails. We divided the enclosure into two halves, east and west of the midpoint, and alternated releases daily between the two areas to minimise residual scent between consecutive trials. Radio-tagged rats or mice were released a total of 96 times at random locations that were unknown to handlers, followed for 50–100 m, then caught and either placed in hidden cages at the end of the scent trail or removed from the forest. Handlers and their dogs had up to 6 h to search for rodents. Key Results. Despite the extremely low density of rodents in the effective research area of 32 ha, both dogs were highly successful at finding rodents, together locating 87% of rats and 80% of mice. Handlers reported few false positive detections. We found that well-trained dogs can effectively cover 30–40 ha of steep forested habitat in half a day (6 h). Conclusions. Despite the limitations of our study design, we conclude that well-trained rodent dogs may be able to locate wild rodents at low densities in forest situations. Implications. Our results support the ongoing use of certified dogs to detect rodent survivors and invaders in conservation areas in New Zealand and elsewhere. Additional research is required to trial dogs on experimentally released wild rodents and to compare the cost-effectiveness of dogs with other detection methods.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Cost- und Monitoring-Projekt: Zwei neue forstliche Meinungsumfragen im Vergleich | The Cost- and Monitoring-Projects: A Comparison of Two New Forestry Opinion Polls

2000 ◽  
Vol 151 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Stephan Wild-Eck ◽  
Willi Zimmermann
Keyword(s):  
Large Scale ◽  
Forest Policy ◽  
Forest Economics ◽  
Positive Attitudes ◽  
Federal Institute ◽  
Swiss Confederation ◽  
Institute Of Technology ◽  
The Cost ◽  
Near Future ◽  
Opinion Polls

Two large-scale surveys looking at attitudes towards forests, forestry and forest policy in the second half ofthe nineties have been carried out. This work was done on behalf of the Swiss Confederation by the Chair of Forest Policy and Forest Economics of the Federal Institute of Technology (ETH) in Zurich. Not only did the two studies use very different methods, but the results also varied greatly as far as infrastructure and basic conditions were concerned. One of the main differences between the two studies was the fact that the first dealt only with mountainous areas, whereas the second was carried out on the whole Swiss population. The results of the studies reflect these differences:each produced its own specific findings. Where the same (or similar) questions were asked, the answers highlight not only how the attitudes of those questioned differ, but also views that they hold in common. Both surveys showed positive attitudes towards forests in general, as well as a deep-seated appreciation ofthe forest as a recreational area, and a positive approach to tending. Detailed results of the two surveys will be available in the near future.

Waste minimization program in Shuaiba Industrial Area

1999 ◽  
Vol 39 (10-11) ◽  
pp. 289-295
Author(s):  
Saleh Al-Muzaini
Keyword(s):  
Solid Waste ◽  
Power Plants ◽  
Large Scale ◽  
Industrial Area ◽  
Waste Minimization ◽  
Regulatory Body ◽  
Eastern Sector ◽  
Western Sector ◽  
Petroleum Companies ◽  
The Cost

The Shuaiba Industrial Area (SIA) is located about 50 km south of Kuwait City. It accommodates most of the large-scale industries in Kuwait. The total area of the SIA (both eastern and western sectors) is about 22.98 million m2. Fifteen plants are located in the eastern sector and 23 in the western sector, including two petrochemical companies, three refineries, two power plants, a melamine company, an industrial gas corporation, a paper products company and, two steam electricity generating stations, in addition to several other industries. Therefore, only 30 percent of the land in the SIA's eastern sector and 70 percent of land in the SIA's western sector is available for future expansion. Presently, industries in the SIA generate approximately 204,000 t of solid waste. With future development in the industries in the SIA, the estimated quantities will reach 240,000 t. The Shuaiba Area Authority (SAA), a governmental regulatory body responsible for planning and development in the SIA, has recognized the problem of solid waste and has developed an industrial waste minimization program. This program would help to reduce the quantity of waste generated within the SIA and thereby reduce the cost of waste management. This paper presents a description of the waste minimization program and how it is to be implemented by major petroleum companies. The protocols employed in the waste minimization program are detailed.

The emerging roles of extracellular vesicles in ovarian cancer

2020 ◽  
Vol 21 ◽  
Author(s):  
Yin-xue Wang ◽  
Yi-xiang Wang ◽  
Yi-ke Li ◽  
Shi-yan Tu ◽  
Yi-qing Wang
Keyword(s):  
Ovarian Cancer ◽  
Extracellular Vesicles ◽  
Large Scale ◽  
Current Treatment ◽  
Detection Methods ◽  
Drug Resistant ◽  
Apoptotic Bodies ◽  
Biological Mechanisms ◽  
Gynecological Malignancy ◽  
Resistance To Chemotherapy

: Ovarian cancer (OC) is one of the deadliest gynecological malignancy. Epithelial ovarian cancer (EOC) is its most common form. OC has both a poor prognosis and a high mortality rate due to the difficulties of early diagnosis, the limitation of current treatment and resistance to chemotherapy. Extracellular vesicles is a heterogeneous group of cellderived submicron vesicles which can be detected in body fluids, and it can be classified into three main types including exosomes, micro-vesicles, and apoptotic bodies. Cancer cells can produce more EVs than healthy cells. Moreover, the contents of these EVs have been found distinct from each other. It has been considered that EVs shedding from tumor cells may be implicated in clinical applications. Such as a tool for tumor diagnosis, prognosis and potential treatment of certain cancers. In this review, we provide a brief description of EVs in diagnosis, prognosis, treatment, drug-resistant of OC. Cancer-related EVs show powerful influences on tumors by various biological mechanisms. However, the contents mentioned above remain in the laboratory stage and there is a lack of large-scale clinical trials, and the maturity of the purification and detection methods is a constraint. In addition, amplification of oncogenes on ecDNA is remarkably prevalent in cancer, it may be possible that ecDNA can be encapsulated in EVs and thus detected by us. In summary, much more research on EVs needs to be perform to reveal breakthroughs in OC and to accelerate the process of its application on clinic.

An Out-of-Core Dataflow Middleware to Reduce the Cost of Large Scale Iterative Solvers

2012 ◽  
Author(s):  
Zheng Zhou ◽  
Erik Saule ◽  
Hasan Metin Aktulga ◽  
Chao Yang ◽  
Esmond G. Ng ◽  
...  
Keyword(s):  
Large Scale ◽  
Iterative Solvers ◽  
The Cost

scholarly journals Insectivorous songbirds as early indicators of future defoliation by spruce budworm

2021 ◽  
Author(s):  
Marion Germain ◽  
Daniel Kneeshaw ◽  
Louis De Grandpré ◽  
Mélanie Desrochers ◽  
Patrick M. A. James ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Spruce Budworm ◽  
Detection Methods ◽  
Maximum Probability ◽  
Point Count ◽  
Probability Of Presence ◽  
Time Lagged ◽  
One Year ◽  
Local Sampling

Abstract Context Although the spatiotemporal dynamics of spruce budworm outbreaks have been intensively studied, forecasting outbreaks remains challenging. During outbreaks, budworm-linked warblers (Tennessee, Cape May, and bay-breasted warbler) show a strong positive response to increases in spruce budworm, but little is known about the relative timing of these responses. Objectives We hypothesized that these warblers could be used as sentinels of future defoliation of budworm host trees. We examined the timing and magnitude of the relationships between defoliation by spruce budworm and changes in the probability of presence of warblers to determine whether they responded to budworm infestation before local defoliation being observed by standard detection methods. Methods We modelled this relationship using large-scale point count surveys of songbirds and maps of cumulative time-lagged defoliation over multiple spatial scales (2–30 km radius around sampling points) in Quebec, Canada. Results All three warbler species responded positively to defoliation at each spatial scale considered, but the timing of their response differed. Maximum probability of presence of Tennessee and Cape May warbler coincided with observations of local defoliation, or provided a one year warning, making them of little use to guide early interventions. In contrast, the probability of presence of bay-breasted warbler consistently increased 3–4 years before defoliation was detectable. Conclusions Early detection is a critical step in the management of spruce budworm outbreaks and rapid increases in the probability of presence of bay-breasted warbler could be used to identify future epicenters and target ground-based local sampling of spruce budworm.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Estimating mouse and rat use in American laboratories by extrapolation from Animal Welfare Act-regulated species

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Larry Carbone
Keyword(s):  
United States ◽  
Animal Welfare ◽  
The United States ◽  
Animal Research ◽  
Government Agencies ◽  
Laboratory Rats ◽  
Tier System ◽  
Western Nations ◽  
Rats And Mice ◽  
Usage Data

AbstractAlone among Western nations, the United States has a two-tier system for welfare protections for vertebrate animals in research. Because its Animal Welfare Act (AWA) excludes laboratory rats and mice (RM), government veterinarians do not inspect RM laboratories and RM numbers are only partially reported to government agencies1. Without transparent statistics, it is impossible to track efforts to reduce or replace these sentient animals’ use or to project government resources needed if AWA coverage were expanded to include them. I obtained annual RM usage data from 16 large American institutions and compared RM numbers to institutions’ legally-required reports of their AWA-covered mammals. RM comprised approximately 99.3% of mammals at these representative institutions. Extrapolating from 780,070 AWA-covered mammals in 2017–18, I estimate that 111.5 million rats and mice were used per year in this period. If the same proportion of RM undergo painful procedures as are publicly reported for AWA-covered animals, then some 44.5 million mice and rats underwent potentially painful experiments. These data inform the questions of whether the AWA needs an update to cover RM, or whether the NIH should increase transparency of funded animal research. These figures can benchmark progress in reducing animal numbers in general and more specifically, in painful experiments. This estimate is higher than any others available, reflecting the challenges of obtaining statistics without consistent and transparent institutional reports.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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