M. S. Norouzzadeh, Anh Totti Nguyen, M. Kosmala
et al.
Significance Motion-sensor cameras in natural habitats offer the opportunity to inexpensively and unobtrusively gather vast amounts of data on animals in the wild. A key obstacle to harnessing their potential is the great cost of having humans analyze each image. Here, we demonstrate that a cutting-edge type of artificial intelligence called deep neural networks can automatically extract such invaluable information. For example, we show deep learning can automate animal identification for 99.3% of the 3.2 million-image Snapshot Serengeti dataset while performing at the same 96.6% accuracy of crowdsourced teams of human volunteers. Automatically, accurately, and inexpensively collecting such data could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences. Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences. Motion-sensor “camera traps” enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.
F. Stephen Dobson, Claire Saraux, David W. Coltman
et al.
Selection analyses of long-term field data frequently use annual comparisons from longlived species with overlapping generations to measure fitness differences with respect to phenotypic characteristics, such as annual phenological timing. An alternative approach applies lifetime estimates of fitness that encompass several annual events. We studied selection on emergence date from hibernation in male and female Columbian ground squirrels, Urocitellus columbianus (Ord 1915). From 32 years of records, we estimated lifetime fitness using either lifetime reproductive success (LRS) or matrix methods, and estimated annual fitness from individual yearly survival and reproduction. We also modified estimates to statistically control for changes in mean population fitness over the lives of individuals. We regressed lifetime fitness metrics on dates of emergence from hibernation, to quantify the strength of selection on emergence date (a heritable trait). All fitness metrics were highly correlated, but differences became apparent when estimating selection coefficients for emergence dates. The annual fitness metric and LRS produced lower effect sizes of selection coefficients than matrix-based lifetime fitness and a lifespan approach based on average annual fitness. Further, only these last two metrics revealed stabilizing selection. These results suggest that the choice of a fitness metric may influence our conclusions about natural selection.
The Lidia cattle breed is featured in several traditional popular bullfighting festivals throughout Spain, including the “Toro de Cuerda” event, in which the animals are subjected to intense physical exercise. However, the physiological impact and welfare implications of these activities remain poorly characterized. This study aimed to evaluate the stress response and muscle damage in Lidia breed bulls during roping bull celebrations through comprehensive blood analysis. Blood samples were collected from 53 adult male Lidia bulls before and after a standardized 45 min continuous running exercise during traditional roping bull events in four Spanish autonomous regions. Hematological parameters, muscle enzymes (creatine kinase, lactate dehydrogenase, lactate), and stress hormones (cortisol and ACTH) were analyzed. Significant increases (<i>p</i> < 0.05) were observed in leukocytes, lymphocytes, monocytes, eosinophils, neutrophils, erythrocytes, hematocrit, hemoglobin, and post-exercise platelets. Muscle enzymes showed marked elevations, with creatine kinase increasing up to 10-fold above baseline values. Stress hormones, cortisol and ACTH, also demonstrated significant increases. Despite the magnitude of these changes, all parameters remained within established reference ranges for the bovine species. This study provides the first physiological assessment of Lidia cattle during popular bullfighting celebrations, establishing baseline data for evidence-based welfare evaluation and management protocols.
Solovyeva Elena, Deorari Rajesh, Pushkarna Gaurav
et al.
The average age of the participants in this research, which evaluated the effects of public display advertising in smart cities, was found to be 31.2 years, with a gender distribution that is balanced. When compared to a prior review, exposure and memory rates showed a 5% improvement in recall rates and a 12% increase in exposure length, suggesting increased advertising effectiveness and reach. Purchase intent increased by 11.8% and interaction levels improved by 10%, according to consumer engagement ratings. In addition, post-exposure attitudes demonstrated a 2.7% improvement in relevance and a 5.4% rise in likeability, highlighting a favorable opinion of public display advertising. These results contribute to the disciplines of urban informatics and advertising effectiveness by providing insightful information on the changing role of public display advertising in the setting of smart cities.
The article analyzes the most important sphere of the economy of any state, which is the agro-industrial complex. It provides production of the most important products necessary for human life and society as a whole. At the same time it is the innovation and implementation of innovation activities that can ensure the competitiveness of the country’s agricultural products in the world arena. The aim of this article is to identify the features of scientific support of innovative development of agro-industrial complex in modern economic conditions. The key priorities of scientific support of the process of implementation of advanced technologies in this area have been considered. The features of the implementation of innovation processes in agriculture have been investigated. The model of innovative development of agriculture of the country is offered.
Andrea Antinucci, Christian Copetti, Giovanni Galati
et al.
We study generalizations of the non-invertible duality defects present in $\mathcal{N} = 4$ SU(N) SYM by studying theories with larger duality groups. We focus on 4d $\mathcal{N} = 2$ theories of class $\mathcal{S}$ obtained by the dimensional reduction of the 6d $\mathcal{N} = (2, 0)$ theory of $A_{N-1}$ type on a Riemann surface $Σ_g$ without punctures. We discuss their non-invertible duality symmetries and provide two ways to compute their fusion algebra: either using discrete topological manipulations or a 5d TQFT description. We also introduce the concept of "rank" of a non-invertible duality symmetry and show how it can be used to (almost) completely fix the fusion algebra with little computational effort.