Social media contain abundant information about the events or news occurring all over the world. Social media growth has a greater impact on various domains like marketing, e-commerce, health care, e-governance, and politics, etc. Currently, Twitter was developed as one of the social media platforms, and now, it is one of the most popular social media platforms. There are 1 billion user’s profiles and millions of active users, who post tweets daily. In this research, buzz detection in social media was (...) carried out by the semantic approach using the condensed nearest neighbor (SACNN). The Twitter and Tom’s Hardware data are stored in the UC Irvine Machine Learning Repository, and this dataset is used in this research for outlier detection. The min–max normalization technique is applied to the social media dataset, and additionally, missing values were replaced by the normalized value. The condensed nearest neighbor (CNN) is used for semantic analysis of the database, and based on the optimized value provided by the proposed method, the threshold is calculated. The threshold value is used to classify buzz and non-buzz discussions in the social media database. The result showed that the SACNN achieved 99% of accuracy, and relative error is less than the existing methods. (shrink)

In this paper, we use Saaty's Eigenvector Method and the Power Method as well as Ω=1, 2, ⋯ , 9, 1/2, 1/3, ⋯ , 1/9} and Ω-={1,2, ⋯ ,9,1, 1/2, ⋯ ,1/9} as the sets from which the pairwise comparison judgments are assigned at random to examine the variation in the values determined for the mean random consistency index. By extensive simulation analysis, we found that both methods produce the same values for the mean random consistency random index. Also, we (...) found that the reason for producing two different sets of values is the use of Ω vs. Ω- and not the selection of the Power Method vs. Saaty's Eigenvector Method. (shrink)

This study compares Australian marketers with those in the United States along lines that are particular to the study of ethics. The test measured two different moral philosophies, idealism and relativism, and compared perceptions of ethical problems, ethical intentions, and corporate ethical values. According to Hofstede''s cultural typologies, there should be little difference between American and Australian marketers, but the study did find significant differences. Australians tended to be more idealistic and more relativistic than Americans and the other results were (...) mixed, making it difficult to generalize about the effects of moral philosophies on the components of ethical decision-making measured here. This is an important finding; as firms become increasingly more globalized, marketers will more often be involved in cross-cultural ethical dilemmas and it seems natural to assume that similar cultures will have similar ethical orientations. That assumption may well prove erroneous. (shrink)

This study adds to the empirical evidence supporting a significant connection between ethics and profitability by examining the connection between published reports of unethical behaviour by publicly traded U.S. and multinational firms and the performance of their stock. Using reports of unethical behaviour published in the Wall Street Journal from 1989 to 1993, the analysis shows that the actual stock performance for those companies was lower than the expected market adjusted returns. Unethical conduct by firms which is discovered and publicized (...) does impact on the shareholders by lowering the value of their stock for an appreciable period of time. Whatever their views on whether ethical behaviour is profitable, managers should be able to see a definite connection between unethical behaviour and the worth of their firm's stock. Stockholders, the press and regulators should find this information important in pressing for greater corporate and managerial accountability. (shrink)

Shared decision-making has become a new focus of health policy. Though its core elements are largely agreed upon, there is little consensus regarding which outcomes to prioritize for policy-mandated shared decision-making.

To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level embodiment level,” the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can (...) be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic bindings make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements. (shrink)

In recent years, multivariate pattern analysis has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging, electroencephalography, and other neuroimaging methodologies. In a similar time frame, “deep learning” has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on (...) linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox intended to facilitate dMVPA for neuroscientists everywhere. (shrink)

v. 1. God, religion, and philosophy; a historical retrospect. 2d ed. 1971.--v. 2. Purushka and prakrita (God and nature). 1st ed. 1968.--v. 3. God and man (nara and Narayan). 1st ed. 1974.--v. 4. Thought; gems in verse: sayings of great saints and thinkers of India. 1st ed. 1975.--v. 5. Truths stranger than fiction. 1st ed.

Meiotic recombination occurs preferentially at certain regions called hot spots and is important for generating genetic diversity and proper segregation of chromosomes during meiosis. Hot spots have been characterized most extensively in yeast, mice and humans. The development of methods based on sperm typing and population genetics has facilitated rapid and high‐resolution mapping of hot spots in mice and humans in recent years. With increasing information becoming available on meiotic recombination in different species, it is now possible to compare several (...) molecular features associated with hot‐spot loci. Further, there have been advances in our knowledge of the factors influencing hot‐spot activity and the role that they play in structuring the genome into haplotype blocks. We review the molecular features associated with hot spots in terms of their properties and mechanisms underlying their function and distribution. A large number of these features seem to be shared among hot spots from different species suggesting common mechanisms for their formation and function. BioEssays 28:45–56, 2006. © 2005 Wiley Periodicals, Inc. (shrink)

A high percent of oxidative energy metabolism is needed to support brain growth during infancy. Unhealthy diets and limited nutrition, as well as other environmental insults, can compromise these essential developmental processes. In particular, iron deficiency anemia has been found to undermine both normal brain growth and neurobehavioral development. Even moderate ID may affect neural maturation because when iron is limited, it is prioritized first to red blood cells over the brain. A primate model was used to investigate the neural (...) effects of a transient ID and if deficits would persist after iron treatment. The large size and postnatal growth of the monkey brain makes the findings relevant to the metabolic and iron needs of human infants, and initiating treatment upon diagnosis of anemia reflects clinical practice. Specifically, this analysis determined whether brain maturation would still be compromised at 1 year of age if an anemic infant was treated promptly once diagnosed. The hematology and iron status of 41 infant rhesus monkeys was screened at 2-month intervals. Fifteen became ID; 12 met clinical criteria for anemia and were administered iron dextran and B vitamins for 1–2 months. MRI scans were acquired at 1 year. The volumetric and diffusion tensor imaging measures from the ID infants were compared with monkeys who remained continuously iron sufficient. A prior history of ID was associated with smaller total brain volumes, driven primarily by significantly less total gray matter and smaller GM volumes in several cortical regions. At the macrostructual level, the effect on white matter volumes was not as overt. However, DTI analyses of WM microstructure indicated two later-maturating anterior tracts were negatively affected. The findings reaffirm the importance of iron for normal brain development. Given that brain differences were still evident even after iron treatment and following recovery of iron-dependent hematological indices, the results highlight the importance of early detection and preemptive supplementation to limit the neural consequences of ID. (shrink)

The majority of commentators agree that the time to focus on embodiment has arrived and that the disembodied approach that was taken from the birth of artificial intelligence is unlikely to provide a satisfactory account of the special features of human intelligence. In our Response, we begin by addressing the general comments and criticisms directed at the emerging enterprise of deictic and embodied cognition. In subsequent sections we examine the topics that constitute the core of the commentaries: embodiment mechanisms, dorsal (...) and ventral visual processing, eye movements, and learning. (shrink)

_The Opportunity Gap_ aims to shift attention from the current overwhelming emphasis on schools in discussions of the achievement gap to more fundamental questions about social and educational opportunity. The achievement gap looms large in the current era of high-stakes testing and accountability. Yet questions persist: Has the accountability movement—and attendant discussions on the achievement gap—focused attention on the true sources of educational failure in American schools? Do we need to look beyond classrooms and schools for credible accounts of disparities (...) in educational outcomes? The essays in this book reintroduce the overlooked central issue in educational inequity: the lack of opportunity that many social groups face in our common quest for educational attainment. In a series of wide-ranging and carefully nuanced essays, _The Opportunity Gap_ casts much-needed light on the vexed relationship between society and education—and on the crucial, persistent role that education plays in addressing social ills. Contributors include Gilberto Q. Conchas, Raewyn Connell, Pat English-Sand, Linda May Fitzgerald, Patresa Hartman, Jeff Howard, Mieko Kamii, Rafa M. Kasim, Christopher Kliewer, Robert A. LeVine, Sarah E. LeVine, Jodi Meyer-Mork, Robert Parris Moses, Sonia Nieto, Donna Raschke, Stephen W. Raudenbush, Ray C. Rist, Beatrice Schnell-Anzola, Irene Serna, Susan McAllister Swap, and Amy Stuart Wells; with an afterword by Ronald F. Ferguson. (shrink)

In this paper, we prove that the lattice of all closure operators of a complete Almost Distributive Lattice L with fixed maximal element m is dual atomistic. We define the concept of a completely meet-irreducible element in a complete ADL and derive a necessary and sufficient condition for a dual atom of Φ (L) to be complemented.

The crystallization behaviour of Fe 70.8 Nb 3.7 Cu 1 Al 2.7 Mn 0.7 Si 13.5 B 7.6 alloy prepared in the form of amorphous ribbons by melt-spinning technique was studied using differential scanning calorimetry and the temperature variation in resistivity. An X-ray diffaction and transmission electron microscopy study showed the formation of f -Fe and/or Fe 3 nanoparticles after the first stage of crystallization. The activation energy for this nanophase formation was 68 kcal mol m 1 . The brittleness (...) of the alloy increased with the formation of nanoparticles after heat treatment. Superior soft magnetic properties were achieved when the material was heat treated at 790 K for 15 min. The particle size at the optimum heat treatment condition for superior soft magnetic properties was found to be 6.0 - 0.5 nm which was less compared than for the Fe-Nb-Cu-Si-B system. The observed coercivity value at the optimum heat treatment condition was found to be 0.32 A m m 1 . The presence of Al in the alloy reduced the particle size and the magnetic anisotropy energy of the system, which resulted in superior soft magnetic properties of the heat-treated materials. (shrink)