3,336 research outputs found

    Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions

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    Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units. The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures. The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system. Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter. The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation

    RECENT ADVANCES IN MOLECULAR MEDICINE AND TRANSLATIONAL RESEARCH

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    ABSTRACT BOO

    Peering into the Dark: Investigating dark matter and neutrinos with cosmology and astrophysics

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    The LCDM model of modern cosmology provides a highly accurate description of our universe. However, it relies on two mysterious components, dark matter and dark energy. The cold dark matter paradigm does not provide a satisfying description of its particle nature, nor any link to the Standard Model of particle physics. I investigate the consequences for cosmological structure formation in models with a coupling between dark matter and Standard Model neutrinos, as well as probes of primordial black holes as dark matter. I examine the impact that such an interaction would have through both linear perturbation theory and nonlinear N-body simulations. I present limits on the possible interaction strength from cosmic microwave background, large scale structure, and galaxy population data, as well as forecasts on the future sensitivity. I provide an analysis of what is necessary to distinguish the cosmological impact of interacting dark matter from similar effects. Intensity mapping of the 21 cm line of neutral hydrogen at high redshift using next generation observatories, such as the SKA, would provide the strongest constraints yet on such interactions, and may be able to distinguish between different scenarios causing suppressed small scale structure. I also present a novel type of probe of structure formation, using the cosmological gravitational wave signal of high redshift compact binary mergers to provide information about structure formation, and thus the behaviour of dark matter. Such observations would also provide competitive constraints. Finally, I investigate primordial black holes as an alternative dark matter candidate, presenting an analysis and framework for the evolution of extended mass populations over cosmological time and computing the present day gamma ray signal, as well as the allowed local evaporation rate. This is used to set constraints on the allowed population of low mass primordial black holes, and the likelihood of witnessing an evaporation

    Cross-phyla protein annotation by structural prediction and alignment

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    Background Protein annotation is a major goal in molecular biology, yet experimentally determined knowledge is typically limited to a few model organisms. In non-model species, the sequence-based prediction of gene orthology can be used to infer protein identity; however, this approach loses predictive power at longer evolutionary distances. Here we propose a workflow for protein annotation using structural similarity, exploiting the fact that similar protein structures often reflect homology and are more conserved than protein sequences. Results We propose a workflow of openly available tools for the functional annotation of proteins via structural similarity (MorF: MorphologFinder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in >90% cases and annotates an additional 50% of the proteome beyond standard sequence-based methods. We uncover new functions for sponge cell types, including extensive FGF, TGF, and Ephrin signaling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls. Conclusions Our work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homologous proteins over long evolutionary distances. We anticipate this will be a powerful approach that boosts discovery in numerous -omics datasets, especially for non-model organisms

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Impacts of coffee fragmented landscapes on biodiversity and microclimate with emerging monitoring technologies

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    Habitat fragmentation and loss are causing biodiversity declines across the globe. As biodiversity is unevenly distributed, with many hotspots located in the tropics, conserving and protecting these areas is important to preserve as many species as possible. Chapter 2 presents an overview of the Ecology of the Atlantic Forest, a highly fragmented biodiversity hotspot. A major driver of habitat fragmentation is agriculture, and in the tropics coffee is major cash crop. Developing methods to monitor biodiversity effectively without labour intensive surveys can help us understand how communities are using fragmented landscapes and better inform management practices that promote biodiversity. Acoustic monitoring offers a promising set of tools to remotely monitor biodiversity. Developments in machine learning offer automatic species detection and classification in certain taxa. Chapters 3 and 4 use acoustic monitoring surveys conducted on fragmented landscapes in the Atlantic Forest to quantify bird and bat communities in forest and coffee matrix, respectively. Chapter 3 shows that acoustic composition can reflect local avian communities. Chapter 4 applies a convolutional neural network (CNN) optimised on UK bat calls to a Brazilian bat dataset to estimate bat diversity and show how bats preferentially use coffee habitats. In addition to monitoring biodiversity, monitoring microclimate forms a key part of climate smart agriculture for climate change mitigation. Coffee agriculture is limited to the tropics, overlapping with biodiverse regions, but is threatened by climate change. This presents a challenge to countries strongly reliant on coffee exports such as Brazil and Nicaragua. Chapter 5 uses data from microclimate weather stations in Nicaragua to demonstrate that sun-coffee management is vulnerable to supraoptimal temperature exposure regardless of local forest cover or elevation.Open Acces

    Acoustic Propagation Variation with Temperature Profile in Water Filled Steel Pipes at Pressure

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    Conventional pressure leak testing of buried pipelines compares measurements of pressure with pipe wall temperature. An alternative proposed method uses acoustic velocity measurements to replace pipe wall temperature measurements. Early experiments using this method identified anomalous results of rising acoustic velocities thought to be caused by air solution. This research investigated the anomalous acoustic velocity measurements by evaluation of acoustic velocity variation with pressure, temperature and air solution. Quiescent air solution rate experiments were carried out in water filled pipes. Computer modelling of the air bubble shape variation with pipe diameter was found to agree with bubble and drop experiments over the pipe diameter range from 100 mm to 1000 mm. Bubbles were found to maintain constant width over a large volume range confirmed by experiments and modelling

    InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

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    This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.Comment: Data and Code: https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVi

    Child Mental Health in Nepal. An epidemiological study of emotional and behavioral problems (EBP) among Nepali schoolchildren reported by parents and teachers

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    Background - There is a substantial gap in our knowledge about the prevalence, magnitude, and correlates of child emotional and behavioral problems (EBPs) in Nepal. Little is known about the agreement between parent and teacher reports on EBP among Nepali children. Therefore, a large-scale survey in the general population was undertaken to fill this gap. Methods - This is a cross-sectional, observational study among schoolchildren aged 6-18 years from 64 schools selected from 16 districts of the three geographical regions of Nepal, including rural, semi-urban, and urban areas. We used the Nepali version of the Child Behavior Checklist (CBCL)/6-18 years completed by parents and the Teacher Report Form (TRF)/6-18 as screening instruments. We used questionnaire to collect background information and assess possible family correlates, including parental education, family structure, migrant worker parents, parental mental and physical illness, family conflicts, and child-rearing reported by parents. The prevalences of child EBPs were computed based on American norms. Comparisons of parent-reported EBPs between genders and across the seven largest castes and ethnic groups were carried out by analysis of variance. The associations between family variables and child internalizing and externalizing problems were analyzed using bivariate correlations and multiple regression. Linear mixed model analysis was used for group comparisons of TRF scale scores and for computing intraclass correlations of teacher reported EBPs. Correlations between the CBCL and TRF scale scores were analyzed using Pearson’s correlation test. Results - The prevalence of parent and teacher reports of Total Problems found in this study were 19.1% and 15.4%, respectively. In both the parent and teacher reports, the prevalence of internalizing problems was higher than that of externalizing problems. The mean CBCL scale scores for Total, Externalizing, and Internalizing problems were 29.7 (standard deviation, SD 25.6), 7.7 (SD 8.0), and 9.1 (SD 8.1). Corresponding mean TRF scale scores were 26.9 (SD 24.5), 6.1 (SD 7.2), and 7.9 (SD 7.3) respectively. For both parent and teacher reports, mean scores for Total Problems and Externalizing Problems were higher among boys than girls. The lowest Hindu caste scored the highest and the indigenous Tharu group scored the lowest on all problem scales. CBCL scores of EBP were higher in the rural areas of the Mountains and Middle Hills regions, whereas in the Tarai region, these scores were higher in the urban areas. The size of the effect was small in all the tests. Using parent reports and bivariate analysis, we found that mental and physical illness in parents, conflict in the family, parental disagreement in child-rearing, and physical punishment of the child correlated positively with both Internalizing Problems and Externalizing Problems. The same associations were found by using multiple regression analysis with small effect size. In addition, parental education, family structure, and migrant worker mothers were associated with Externalizing Problems. Cross-informant agreement between parents and teachers was moderate (r=.38). The agreement for Externalizing Problems was r = .37 and the agreement for Internalizing Problems was r = .34. Moderate to low correlations were found for all syndrome scales, with coefficients ranging from r=.26 (Social Problems) to r=.37 (Attention Problems). The effect of child gender on parent-teacher agreement was significant only for Internalizing Problems, with a higher agreement for girls than for boys with small effect sizes. Conclusion - Our study showed that child EBP scores varied according to gender, caste/ethnic group, and living area. The cross-informant agreement between parents and teachers was found to be moderate and varied according to the type of problems and the child’s gender. Further, the study found that child mental health problems were associated with several family risk factors. Finally, the study points to the need to acquire information from multiple sources when assessing children with mental health problems
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