Miracle-dz/UniFAR-FTUs huggingface dataset:
GEOTECHNICAL ZONATION OF THE CATANIA SOILS AND EVALUATION OF THE LIQUEFACTION HAZARD 2

It is well known that local geological and topographic conditions play a major role on earthquake ground motions and distribution of damage. This aspect becomes even more critical in areas with sharp transitions between stiff surface formations and softer soil materials, typically found in a volcanic zone like Catania which lies at the foot of the Mt. Etna volcano and was affected by many eruptions in historical and prehistoric times. Hence, a substantial effort was devoted to the zonation and seismic characterisation of soils in the Catania area. This task was carried out both by exploiting the existing data and by performing special investigations at some selected sites. The creation of a database of about 860 boreholes provided the basis for a geotechnical zonation of the Catania municipal area; based on existing in situ tests (SPT, CPT, cross-hole and down-hole measurements) the mapping of the shear wave velocity for earthquake engineering purposes was carried out. All the borehole and in situ test data were stored in the database and the investigation sites located via GIS system (see 2.2). To integrate the previous data, investigations were carried out for measuring the shear wave velocity by surface wave methods (see 2.3), in particular SASW investigations have been performed at beach sites south of the city (La Plaja), where saturated sand deposits exhibit a marked susceptibility to liquefaction. Laboratory cyclic tests were performed to characterise the non linear soil behaviour, which may occur during strong earthquakes, such as the 1693 scenario earthquake (see 1). Two boreholes were made, in which down-hole velocity measurements have been executed, and undisturbed samples were retrieved. Resonant column tests and cyclic loading torsional shear tests were performed to investigate the initial shear modulus G o and shear modulus degradation with shear strain, as well as damping ratio degradation (2.4). Land vulnerability to earthquake-triggered phenomena was also investigated, mainly from the viewpoint of landslide and liquefaction hazard. For the landslide problem, one significant case was considered (Monte Po slide), but the data available are insufficient and it will not be treated in the following. On the other hand, soil liquefaction effects were reported by historical sources following the 1693 and 1818 earthquakes (see 2.5). The susceptibility of a site to seismic-induced liquefaction may be assessed comparing the cyclic soil resistance to the cyclic shear stresses induced by the ground motion. The latter is of course a function of the …
Finite element tree crown hydrodynamics model (FETCH) using porous media flow within branching elements: A new representation of tree hydrodynamics

Estimating transpiration and water flow in trees remains a major challenge for quantifying water exchange between the biosphere and the atmosphere. We develop a finite element tree crown hydrodynamics (FETCH) model that uses porous media equations for water flow in an explicit three‐dimensional branching fractal tree‐crown system. It also incorporates a first‐order canopy‐air turbulence closure model to generate the external forcing of the system. We use FETCH to conduct sensitivity analysis of transpirational dynamics to changes in canopy structure via two scaling parameters for branch thickness and conductance. We compare our results with the equivalent parameters of the commonly used resistor and resistor‐capacitor representations of tree hydraulics. We show that the apparent temporal and vertical variability in these parameters strongly depends on structure. We suggest that following empirical calibration and validation, FETCH could be used as a platform for calibrating the “scaling laws” between tree structure and hydrodynamics and for surface parameterization in meteorological and hydrological models.
Biological Monitoring: A Bayesian Model for Multivariate Compositional Data

This article develops a model to relate a multivariate compositional response to a number of covariates and proposes a new graphical model, called the Random Effects Discrete Regression (REDR) model, which allows for examination of the complex conditional relationships between a set of covariates and multiple discrete response variables. The approach offers a number of advantages over previous approaches and allows for a wide range of inferences. Relationships between compositional observations can be evaluated through a set of interaction parameters and inference about the influence of covariates is possible through a set of regression coefficients. The model also allows for examination of relationships between the covariates via another set of interactions. Parameter inference via Bayesian methods and MCMC is discussed. The proposed model and MCMC methods are used to examine the relationship between compositional observations of two characteristics of fish species and a number of covariates. These relationships are of interest to the U.S. Environmental Protection Agency for stream monitoring.
Component Interoperability

Component-based software development is gaining recognition as the key technology for the construction of high-quality, evolvable, large software systems in timely and affordable manners. In this new setting, interoperability is one of the essential issues, since it enables the composition of reusable heterogeneous components developed by different people, at different times, and possibly with different uses in mind. Currently most object and component platforms (such as CORBA, DCOM, or EJB) already provide the basic infrastructure for component interoperability at the lower levels, i.e., they sort out most of the “plumbing” issues. However, interoperability goes far beyond that; it also involves behavioral compatibility, protocol compliance, agreements on the business rules, etc. This chapter tries to go through the basic concepts related to component interoperability, with special emphasis in the syntactic, protocol and operational specifications of components. Our main goal is to point out the existing problems, survey the current solutions and how they address those problems, and to draw attention towards some of the still open issues and challenges in this interesting research area.
Designing a Predictable Internet Backbone Network

Designing a backbone network is hard. On one hand, users expect the network to have very high availability, little or no congestion, and hence little or no queueing delay. On the other hand, traffic conditions are always changing. Over time usage patterns evolve, customers come and go, new applications are deployed, and the traffic matrices of one year are quite different from the next. Yet the network operator must design for low congestion over the multiple years that the network is in operation. Harder still, the network must be designed to work well under a variety of link and router failures. It is not surprising that most networks today are enormously overprovisioned, with typical utilizations around 10%. In this paper we propose that backbone networks use Valiant Load-balancing over a fully-connected logical mesh. This is quite a radical departure from the way backbones are built today, and raises as many questions as it answers. But it leads to a surprisingly simple architecture, with predictable and guaranteed performance, even when traffic matrices change and when links and routers fail. It is provably the lowest capacity network with these characteristics. In addition, it provides fast convergence after failure, making it possible to support real-time applications.
A Self-Absorption Census of Cold H I Clouds in the Canadian Galactic Plane Survey

We present a 21 cm line H I self-absorption (HISA) survey of cold atomic gas within Galactic longitudes l = 75° to 146° and latitudes b = -3° to +5°. We identify HISA as spatially and spectrally confined dark H I features and extract it from the surrounding H I emission in the arcminute-resolution Canadian Galactic Plane Survey (CGPS). We compile a catalog of the most significant features in our survey and compare our detections against those in the literature. Within the parameters of our search, we find nearly all previously detected features and identify many new ones. The CGPS shows HISA in much greater detail than any prior survey and allows both new and previously discovered features to be placed into the larger context of Galactic structure. In space and radial velocity, faint HISA is detected virtually everywhere that the H I emission background is sufficiently bright. This ambient HISA population may arise from small turbulent fluctuations of temperature and velocity in the neutral interstellar medium. By contrast, stronger HISA is organized into discrete complexes, many of which follow a longitude-velocity distribution that suggests that they have been made visible by the velocity reversal of the Perseus arm's spiral density wave. The cold H I revealed in this way may have recently passed through the spiral shock and be on its way to forming molecules and, eventually, new stars. This paper is the second in a series examining HISA at high angular resolution. A companion paper (Paper III) describes our HISA search and extraction algorithms in detail.
TrustE: An Emotional Trust Model for Agents

Trust and reputation have been proposed in the Multiagent Systems area as a way to assist agents to select good partners in order to improve interactions between them. As well as trust, agent emotions have been studied with the aim of making the actions and reactions of the agents more like those of humans. In this paper, we present a trust model based on emotions. It is a hybrid mathematical and symbolical model designed to capture the complexity of reasoning. Quantitative and qualitative evaluations are combined through the incorporation of some emotional elements in the trust evaluation.
Power aware decoding of a scalable video bit-stream

Due to an increasing trend of mobile multimedia services, consideration of power is crucial since portable devices have limited battery power inherently. In this paper we have proposed a power adaptive decoding scheme which makes efficient use of scalable (layered) bit-stream. The proposed decoder has a novel ability to either decode whole bit stream (all layers) or a portion of the bit stream (few layers) depending upon the available power. Experimental results show that the decoding time of proposed framework outperformed that of standard H.264 decoder by 86% for foreman sequence, in low battery levels.
A Comparative Study of Power Amplifiers' Sensitivity to Load Mismatch: Single Branch vs. Doherty Architectures

This paper proposes a comparative study of the sensitivity of different power amplifiers architectures to load mismatch. The two considered architectures are a single branch class AB power amplifier, and a multi-branch architecture using the Doherty dynamic load modulation technique. Two LDMOS based power amplifiers operating around 1.95 GHz are used. The magnitude of the load reflection coefficient is varied from 0 to 0.2 in steps of 0.05. For each of these values, the phase of the load reflection coefficient is swept over 360° in steps of 45°. The amplifiers' linearity and power added efficiency variations with the load reflection coefficients are evaluated. It is found that the Doherty amplifier is less sensitive to load mismatch than a single branch class AB amplifier in terms of its efficiency performance. However, the linearity performance of the single branch class AB amplifier is less sensitive to the load mismatch than that of the Doherty amplifier.
Processing Continuous Range Queries with Spatiotemporal Tolerance

Continuous queries are often employed to monitor the locations of mobile objects (MOs), which are determined by sensing devices like GPS receivers. In this paper, we tackle two challenges in processing continuous range queries (CRQs): coping with data uncertainty inherently associated with location data, and reducing the energy consumption of battery-powered MOs. We propose the concept of spatiotemporal tolerance for CRQ to relax a query's accuracy requirements in terms of a maximal acceptable error. Unlike previous works, our definition considers tolerance in both the spatial and temporal dimensions, which offers applications more flexibility in specifying their individual accuracy requirements. As we will show, these tolerance bounds can provide well-defined query semantics in spite of different sources of data uncertainty. In addition, we present efficient algorithms that carefully control when an MO should sense or report a location, while satisfying these tolerances. Thereby, we particularly reduce the number of position sensing operations substantially, which constitute a considerable source of energy consumption. Extensive simulations confirm that the proposed algorithms result in large energy savings compared to nontolerant query processing.
Application of evolutionary algorithm in optimizing the fuzzy rule base for nonlinear system modeling and control

Fuzzy systems generally work based on expert knowledge base. Fuzzy expert knowledge base incorporates human knowledge through fuzzy rules and fuzzy membership functions. In designing fuzzy models, a major difficulty in the identification of an optimized fuzzy rules as well as membership function shape and type of an individual rule. In fact, it is difficult and time consuming for an expert to define a complete rule set for a complex system having a large number of parameters. In this paper, we proposed a flexible encoding method for evolutionary algorithm to discover parameters of fuzzy rule, design of a suitable fuzzy models and controller for a particular environment. In Evolutionary Fuzzy System (EFS), evolutionary algorithms are adapted for finding the optimal fuzzy rule sets including the number of rules inside it and selecting the membership function shape and type of each individual rule in two different ways respectively. The benefits of this methodology are illustrated for the modeling and control of nonlinear system that shows better performance than existing fuzzy expert systems.
RSAE: Ranked Keyword Search over Asymmetric Encrypted Cloud Data

Cloud computing becomes more and more popular and is applied into many practical applications because of much cheaper and more powerful features. In cloud system, users can outsource local data to the cloud servers to lighten their local storage and computing resource loads, which products a new industry method of use-on-demand, pay-on-use. However outsourcing data to cloud servers makes sensitive information centralized into the server, which brings great challenge to protecting sensitive information privacy. For privacy preserving, the user encrypts sensitive data before outsourcing. Traditional searchable encryption methods make it possible for users to securely conduct keyword search over encrypted data and finally retrieve the most relevant Top-N files among the whole data. In this paper, we systematically propose a scheme to solve the problem of how to securely and efficiently retrieve the Top-N files by keyword-based searching over encrypted data by asymmetric encrypted.
The Concept of Visual Classes for Object Classification

The article introduces the concept of visual classes as a general framework for object classiication. Visual classes group together appearances which are similar with respect to a set of image measurements. As deened here, visual classes are implicit in many object representation schemes (geometric as well as appearance based models). We argue that the identiication of visual classes provides a powerful tool for object classiication. They provide a rst step to classiication depending on information provided for recognition, including context dependency and relations in space and time between objects. The article introduces a statistical object representation which can be seen as a generalization of various object representations. Based on this statistical representation, the article introduces a possible extraction and representation of visual classes. First experimental results are given in order to validate the concept.
A novel method of recovering camera parameter from dynamic texture video

Dynamic texture is a part of the natural scene. Video augmentation is research hotspot in the field of computer vision and even robot research area. Robust camera tracking plays a key role in augmented video. It is an important issue in dynamic texture video augmentation. We propose an efficient and robust approach to structure and motion recovery for dynamic texture video augmentation. First we present an efficient level set method to segment dynamic texture from video. Based on this, a new method of recovering camera parameter is put forward. Experiments show that the proposed method is very efficient in recovering camera parameter in the dynamic texture video and can overcome more error of traditional method.
A method of modeling and service encapsulation on cloud logistics resources

The resources that enable cloud logistics have the characteristics of heterogeneity, discrete distribution, dynamics and autonomy. They have an influence on the efficiency of the integration and configuration of the resources. Based on fully considering the classification and the features of the cloud logistics resources, this paper establishes a uniform resource expression model. This model does not only transform the heterogeneity and discrete distribution into attribute or function information, but also transforms the dynamic and autonomy characteristics into capability information, achieved the mapping from cloud logistics physical resources to virtual resources. The use of ontology and web service technology encapsulated virtual resources into a Web service achieves the cloud service encapsulation of logistics resources. Finally, a transportation service application case has been used to validate the feasibility of the method.
Difference in the Incidence Trend of Nasopharyngeal and Oropharyngeal Carcinomas in Taiwan: Implication from Age-Period-Cohort Analysis

Lifestyle factors are considered important for the pathogenesis of both nasopharyngeal and oropharyngeal carcinomas. In Taiwan, the incidence of nasopharyngeal carcinoma gradually decreased over the past 20 years, whereas that of oropharyngeal carcinoma increased rapidly. To compare the incidence trends of nasopharyngeal and oropharyngeal carcinomas in Taiwan, the age-period-cohort model was used to analyze epidemiologic data from 1981 to 2000 obtained from the Taiwan Cancer Registry. The calendar time period of 1986 to 1990 and the 1931 to 1940 birth cohort were used as reference groups for estimates of relative risk. For nasopharyngeal carcinoma, the incidence seemed to decrease in most age groups and was more prominent in women (30%) than in men (23%). For oropharyngeal carcinoma, the incidence increased in all age groups and was more prominent in men (391.4%) than in women (59.2%). Cohort effect was found for both nasopharyngeal and oropharyngeal carcinomas. The relative risk of nasopharyngeal carcinoma for the 1971 to 1980 birth cohort was 0.38 for women and 0.68 for men. The relative risk of oropharyngeal carcinoma for the 1971 to 1980 cohort was 45.67 for men and 2.69 for women. Change in lifestyle seemed to be an important factor for the difference in the incidence trend between nasopharyngeal and oropharyngeal carcinomas and between men and women. (Cancer Epidemiol Biomarkers Prev 2006;15(5):856–61)
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr. usgs.gov/landdaac/glcc/glcc.html.