Where Are We Going? - Attitudes Towards Migrations in Archaeological Thought

Migrations have always been a much debated topic in archaeology. In the first hal f of the 20th century migrations were a common explanation for change in the material culture, but they were also used in a nationalistic and chauvinistic way. After the Second World War a reaction came and many archaeologists rejected migrations as a relevant explanation, whereby migrations more or less disappeared from the archaeological discourse. Such changes in the archaeological interest could be linked to public opinions of the times, as well as to reactions within the discipline. In conclusion it is argued that a discussion about past migrations is important, not least in order to question prevailing ideas about a cultural homogeneity or "pureness" in the past.

Stochastic events can explain sustained clustering and polarisation of opinions in social networks

Understanding the processes underlying development and persistence of polarised opinions has been one of the key challenges in social networks for more than two decades. While plausible mechanisms have been suggested, they assume quite specialised interactions between individuals or groups that may only be relevant in particular contexts. We propose that a more broadly relevant explanation might be associated with the influence of external events. An agent-based bounded-confidence model has been used to demonstrate persistent polarisation of opinions within populations exposed to stochastic events (of positive and negative influence) even when all interactions between individuals are noisy and assimilative. Events can have a large impact on the distribution of opinions because their influence acts synchronistically across a large proportion of the population, whereas an individual can only interact with small numbers of other individuals at any particular time.

Analysis of Differences in Gene Expression Associated with Variation in Biomass Composition in Sugarcane

Sugarcane has a high potential to support second-generation ethanol production and environmentally friendly by-products for use in chemical, pharmaceutical, medical, cosmetic and food industries. A crucial challenge for a long-term economic viability is to optimise the crop for production of a biomass composition that will ensure maximum economic benefit. Transcriptome data analysis provides a relevant explanation of phenotypic variances and gives a more accurate prediction of phenotypes than genomic information. This multi-omic approach, with an integrated transcriptomics and metabolomics analysis may reveal details of biological mechanisms and pathways. A global view of transcriptional regulation and the identification differentially expressed genes (DEGs) and metabolites may help the feasibility of tailoring engineering targeted biosynthetic pathways to improve the production of these bio-products from sugarcane. We propose a profiling analysis workflow (pipeline) to generate empirical correlations between gene expression, metabolites, proteins and phenotypic traits and pathway analysis, with a highlight focus on data visualisation. This study of genetic variation in gene expression and correlations with metabolic and protein phenotype relies on high-throughput methodology, measurement and analysis of 360 samples, 24 commercial sugarcane cultivars with different phenotypic characteristics at 5 different development stages with 3 replicates.

Singular Mean-Field States: A Brief Review of Recent Results

This article provides a focused review of recent findings which demonstrate, in some cases quite counter-intuitively, the existence of bound states with a singularity of the density pattern at the center; the states are physically meaningful because their total norm converges. One model of this type is based on the 2D Gross–Pitaevskii equation (GPE), which combines the attractive potential ∼ r − 2 and the quartic self-repulsive nonlinearity, induced by the Lee–Huang–Yang effect (quantum fluctuations around the mean-field state). The GPE demonstrates suppression of the 2D quantum collapse, driven by the attractive potential, and emergence of a stable ground state (GS), whose density features an integrable singularity ∼ r − 4 / 3 at r → 0 . Modes with embedded angular momentum exist too, but they are unstable. A counter-intuitive peculiarity of the model is that the GS exists even if the sign of the potential is reversed from attraction to repulsion, provided that its strength is small enough. This peculiarity finds a relevant explanation. The other model outlined in the review includes 1D, 2D, and 3D GPEs, with the septimal (seventh-order), quintic, and cubic self-repulsive terms, respectively. These equations give rise to stable singular solitons, which represent the GS for each dimension D, with the density singularity ∼ r − 2 / ( 4 − D ) . Such states may be considered the results of screening a “bare” delta-functional attractive potential by the respective nonlinearities.

What's Next in Love and Sex

What’s Next in Love and Sex is a comprehensive examination of contemporary academic findings relating to all matters of the mind, body, and heart in the modern world. Written by one of the pioneers of love and sex research, Dr. Hatfield, along with her colleagues Dr. Rapson and Dr. Purvis, this book relies on contemporary scientific findings to provide an updated and relevant explanation for why we do the things we do when we’re in love, searching for love, making love, or attempting to keep a faltering relationship together. It addresses such topics as the role of social media in love and sex, the hookup generation, robots, avatars, fantasy sex, virtual pornography, interactive sex, and the future, as well as the benefits, and pain of love. This book will give young people an in-depth scientific understanding of contemporary love and sex while still providing a light-hearted, accessible, and entertaining read.

Monocausalidade e teoria da crise: uma resposta a David Harvey

Michael Roberts oferece uma crítica abrangente a Harvey e argumenta que Marx nunca abandonou a lei da queda tendencial da taxa de lucro como uma explicação relevante para as crises. Ele nunca voltou à teoria simplesmente porque estava satisfeito com ela. Ao invés de desenvolver a teoria, tentou descobrir como usá-la para explicar a natureza cíclica do capitalismo bem como sua natureza transitória. Roberts defende que os pressupostos da lei são realistas e podem ser reduzidos a apenas dois: que a força de trabalho é a única fonte de valor e que a acumulação de capital leva ao aumento da composição orgânica do capital. O autor se opõe à visão de que cada crise tenha uma origem diferente e “conjuntural”. A natureza recorrente das crises capitalistas implica que tenham uma causa comum.ABSTRACTMichael Roberts offers a comprehensive critique of Harvey and argues that Marx never abandoned the TRPF as a relevant explanation of crises. He never went back to the theory in his later years simply because he was satisfied with it. Rather than developing the theory he tried to figure out how to use it to explain the cyclical nature of capitalism as well as its transitory nature. Roberts contends that Marx’s assumptions for the TRPF are realistic and can be reduced to just two: labor power is the only source of value, and capital accumulation leads the organic composition of capital to rise. He argues against the view that each crisis has a different or ‘conjunctural’ origin. The recurrent nature of capitalist crises implies that they must have a common cause. Tradução: Cássio Arruda Boechat ([email protected])

Groundwater resources at salinisation risk: effects of climate and utilisation changes in the case of Apulian coastal aquifers (Southeastern Italy)

Seawater intrusion is the main cause of groundwater salinisation in Italy. The largest coastal aquifers, highly vulnerable to salinisation, are in Apulia. For these aquifers, main changes in terms of climate change and utilisation are discussed together with piezometric trends, as the latter are relevant triggering factors for upconing and lateral seawater intrusion. For this purpose, time series from 1921 to 2016 concerning climate (rainfall and temperature), from 1965 to 2016 concerning groundwater availability (piezometric values), and recent periodic data on potable utilisation are discussed. Climate and groundwater availability trends at 2016 are compared with trends previously assessed, using the same dataset (1921-2001 for rainfall and temperature). The negative characteristic of rainfall 1921-2001 trend improved in the next years up to disappear in the assessment of rainfall 1921-2016 trend. Notwithstanding the improving of rainfall trend and the reduction of groundwater utilisation, both observed at 2016, the improvement of piezometric trends at 2016 is not enough to remove a prevailing decreasing trend, previously observed. The increases of temperature and effective evapotranspiration should be considered a relevant explanation of groundwater availability reduction. The consequence of these results should be quickly considered in the management of groundwater resources.

Exact Algorithms for MRE Inference

Most Relevant Explanation (MRE) is an inference task in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given evidence by maximizing the Generalized Bayes Factor (GBF). No exact MRE algorithm has been developed previously except exhaustive search. This paper fills the void by introducing two Breadth-First Branch-and-Bound (BFBnB) algorithms for solving MRE based on novel upper bounds of GBF. One upper bound is created by decomposing the computation of GBF using a target blanket decomposition of evidence variables. The other upper bound improves the first bound in two ways. One is to split the target blankets that are too large by converting auxiliary nodes into pseudo-targets so as to scale to large problems. The other is to perform summations instead of maximizations on some of the target variables in each target blanket. Our empirical evaluations show that the proposed BFBnB algorithms make exact MRE inference tractable in Bayesian networks that could not be solved previously.