The dimensions of personality in humans and other animals: A comparative and evolutionary perspective

This paper considers the structure and proximate mechanisms of personality in humans and other animals. Significant similarities were found between personality structures and mechanisms across species in at least two broad traits: Extraversion and Neuroticism. The factor space tapped by these personality dimensions is viewed as a general integrative framework for comparative and evolutionary studies of personality in humans and other animals. Most probably, the cross-species similarities between the most broad personality dimensions like Extraversion and Neuroticism as well as other Big Five factors reflect conservative evolution: constrains on evolution imposed by physiological, genetic and cognitive mechanisms. Lower-order factors, which are more species- and situation-specific, would be adaptive, reflecting correlated selection on and trade-offs between many traits

Big Data approaches as a support for precision livestock farming techniques

With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future

Big Bang nucleosynthesis revisited via Trojan Horse Method measurements

Nuclear reaction rates are among the most important input for understanding the primordial nucleosynthesis and therefore for a quantitative description of the early Universe. An up-to-date compilation of direct cross sections of 2H(d,p)3H, 2H(d,n)3He, 7Li(p,alpha)4He and 3He(d,p)4He reactions is given. These are among the most uncertain cross sections used and input for Big Bang nucleosynthesis calculations. Their measurements through the Trojan Horse Method (THM) are also reviewed and compared with direct data. The reaction rates and the corresponding recommended errors in this work were used as input for primordial nucleosynthesis calculations to evaluate their impact on the 2H, 3,4He and 7Li primordial abundances, which are then compared with observations.Comment: 22 pages, 7 figures, accepted for publication in The Astrophysical Journa

Reconciling Environmental Justice with Climate Change Mitigation: A Case Study of NC Swine CAFOs

For thirty years, the swine industry has externalized severe environmental and health harms onto poor communities of color in Eastern North Carolina. This “Big Pig” problem is caused by the confinement, consolidation, and concentration of industrial hog operations within the low, flat, and economically marginalized Coastal Plain. Big Pig’s rise was not inevitable. As recently as 1982, more than 11,000 small swine farms freckled nearly all of North Carolina’s 100 counties. Then came the “boom” of consolidation and industrialization that transformed hog production into a highly consolidated and vertically integrated industry

This little piggy went to market : will the new pork industry call the Heartland home?

Throughout the 1990s, the pork industry has been at the forefront of a revolution in the structure of the U.S. food and agricultural sector. In particular, the pork industry has been rapidly moving away from its traditional structure built on hundreds of thousands of small farms selling hogs at local terminal markets to a much more concentrated "supply chain" model. Contracting is one prominent feature of supply chains, and the share of pork production grown under contract or vertical integration has jumped from a few percent in the early 1980s to around a third today. Most analysts agree that the structure of the U.S. pork industry will soon resemble that of the U.S. poultry industry, which moved to a supply chain structure more than three decades ago. In short, the hog industry, once a quintessential "family farm" enterprise, has gone to market---a very big market.> As the pork industry's structure has changed, so has its geography. Raising hogs was once heavily concentrated in the Corn Belt, since corn is the primary feed for hogs. The shift to supply chains, however, has taken the pork industry to many new places. North Carolina and Virginia became major pork states in the 1980s. More recently, the industry has moved aggressively into states in the Great Plains that used to be cattle country, Oklahoma being a good case in point. Pork production there has leaped nearly 900 percent since 1990. Where the pork industry locates in the future carries big economic implications.> Drabenstott examines the changes taking place in the U.S. pork industry. He concludes that the recent geographic shift in the industry could foreshadow still more shifts in the future, possibly including moves to Canada, Mexico, or South America.Animal industry ; Federal Reserve District, 10th

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well