Miracle-dz/UniFAR-FTUs huggingface dataset:
Perfusion Scintigraphy Versus 256-Slice CT Angiography in Pregnant Patients Suspected of Pulmonary Embolism: Comparison of Radiation Risks

One aim of the current study was to determine normalized dose data for maternal radiosensitive organs and embryo/fetus from 256-slice CT pulmonary angiography (CTPA) performed on pregnant patients suspected of having pulmonary embolism. A second aim was to provide reliable maternal and fetal doses and associated radiation cancer risk estimates from 256-slice CTPA and lung perfusion scintigraphy (LPS) for comparison. Methods: Mathematic anthropomorphic phantoms were generated to simulate the average woman at early pregnancy and at the third, sixth, and ninth months of gestation. In each phantom, 0–3 additional 1.5-cm-thick fat tissue layers were added to derive 4 phantoms representing pregnant women with different body sizes. Monte Carlo methods were used to simulate low-dose 256-slice CTPA exposures on each of the 16 generated phantoms. Normalized organ and embryo/fetal dose data were derived for exposures at 80, 100, and 120 kV. Maternal effective dose and embryo/fetal dose from 256-slice CTPA and associated lifetime attributable risks of radiation cancer were determined for different body sizes and gestational stages and compared with corresponding data from LPS. Results: For an average-sized pregnant patient at the first trimester, the 256-slice CTPA exposure resulted in a maternal effective dose of 1 mSv and an embryo/fetal dose of 0.05 mGy. However, maternal effective dose considerably increased with body size, whereas embryo/fetal dose increased with both body size and gestational stage. Compared with LPS, low-dose CTPA to an average-sized pregnant patient resulted in a 30% higher maternal effective dose but a 3.4–6 times lower embryo/fetal dose. Nevertheless, LPS was associated with less aggregated radiation risk for an average-sized pregnant patient, with the difference from CTPA being increased further for larger patients. Conclusion: Compared with CTPA performed with a modern wide-area CT scanner, LPS remains comparatively more dose-efficient.
Multiphase flow: models for nuclear, fossil, and biomass energy production

The object of this review is to illustrate how the transport phenomena approach can be used to solve many important problems in the emerging new energy technology of multiphase flow. Two broad applications of transient multiphase flow models are discussed. They are: modeling of the loss of coolant accident and small breaks in the nuclear steam supply systems; and modeling for scale-up of fluidized bed and entrained bed combustors, gasifiers and reactors in fossil energy and biomass conversion processes. (DMC)
Fate of a mutation in a fluctuating environment

Significance Evolution in variable environments depends crucially on the fates of new mutations in the face of fluctuating selection pressures. In constant environments, the relationship between the selective effect of a mutation and the probability that it will eventually fix or go extinct is well understood. However, our understanding of fixation probabilities in fluctuating environmental conditions is limited. Here, we show that temporal fluctuations in environmental conditions can have dramatic effects on the fate of each new mutation, reducing the efficiency of natural selection and increasing the fixation probability of all mutations, including those that are strongly deleterious on average. This makes it difficult for a population to maintain specialist adaptations, even if their benefits outweigh their costs. Natural environments are never truly constant, but the evolutionary implications of temporally varying selection pressures remain poorly understood. Here we investigate how the fate of a new mutation in a fluctuating environment depends on the dynamics of environmental variation and on the selective pressures in each condition. We find that even when a mutation experiences many environmental epochs before fixing or going extinct, its fate is not necessarily determined by its time-averaged selective effect. Instead, environmental variability reduces the efficiency of selection across a broad parameter regime, rendering selection unable to distinguish between mutations that are substantially beneficial and substantially deleterious on average. Temporal fluctuations can also dramatically increase fixation probabilities, often making the details of these fluctuations more important than the average selection pressures acting on each new mutation. For example, mutations that result in a trade-off between conditions but are strongly deleterious on average can nevertheless be more likely to fix than mutations that are always neutral or beneficial. These effects can have important implications for patterns of molecular evolution in variable environments, and they suggest that it may often be difficult for populations to maintain specialist traits, even when their loss leads to a decline in time-averaged fitness.
Intrinsic spectral analysis based on temporal context features for query-by-example spoken term detection

We investigate the use of intrinsic spectral analysis (ISA) for query-by-example spoken term detection (QbE-STD). In the task, spoken queries and test utterances in an audio archive are converted to ISA features, and dynamic time warping is applied to match the feature sequence in each query with those in test utterances. Motivated by manifold learning, ISA has been proposed to recover from untranscribed utterances a set of nonlinear basis functions for the speech manifold, and shown with improved phonetic separability and inherent speaker independence. Due to the coarticulation phenomenon in speech, we propose to use temporal context information to obtain the ISA features. Gaussian posteriorgram, as an efficient acoustic representation usually used in QbE-STD, is considered a baseline feature. Experimental results on the TIMIT speech corpus show that the ISA features can provide a relative 13.5% improvement in mean average precision over the baseline features, when the temporal context information is used. Index Terms: spoken term detection, intrinsic spectral analysis, Gaussian posteriorgram, dynamic time warping
Tensile behaviour of radiata pine with different moisture contents at elevated temperatures

Abstract The aim of this study was to obtain tensile elastic modulus (EM) information for radiata pine (Pinus radiata D. Don) sapwood in tangential grain direction, over a temperature range of 70°C to 150°C for a wide range of moisture contents. Such information is scarce, probably because of difficulties with research equipment design and process control strategies to perform accurate tests. As expected, EM dramatically decreased with increasing temperature and moisture content. The results were modelled to yield a relationship between stress and strain. The results were also successfully transposed into a mastercurve based on temperature-moisture equivalence through a modified form of the Williams, Landel, and Ferry equation for amorphous polymers. This result is consistent with the view that wood is visco-plastic around the glass transition zone of the ligno-hemicellulosic matrix. It is demonstrated that moisture and temperature can play a significant role in reducing stress during drying, regardless of the drying time. Properties of wood, such as tensile elastic information at elevated temperatures, are important for mechanical design, distortion modelling and understanding the fundamental behaviour of wood in general.
New Trends on Exploratory Methods for Data Analytics

Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes cumbersome. Thus, being able to cast exploratory queries in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so called example-based methods, in which the user, or the analyst circumvent query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful both in cases where a user is looking for information in an unfamiliar dataset, or simply when she is exploring the data without knowing what to find in there. In this tutorial, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.
In vitro oxidative footprinting provides insight into apolipoprotein B‐100 structure in low‐density lipoprotein

Low‐density lipoprotein (LDL) is a major cholesterol carrier in human blood. Oxidations of apolipoprotein B‐100 (apo B‐100, LDL protein) could be proatherogenic and play critical roles in early stages of plaque formation in the arterial wall. The structure of apo B‐100 is still poorly understood, partially due to its size (550 KDa, 4563 amino acids). To gain an insight into LDL structure, we mapped the regions of apo B‐100 in human LDL that were prone to oxidation using peroxynitrite and hypochlorite as probes. In this study, LDL was incubated with various concentrations of peroxynitrite and sodium hypochlorite in bicarbonate buffer. The LDL protein apo B‐100 was delipidated, denatured, alkylated, and subjected to tryptic digestion. Tryptic peptides were analyzed employing LC‐MS/MS. Database search was performed against the apo B‐100 database (SwissProt accession #P04114) using “SEQUEST” algorithm to identify peroxynitrite and hypochlorite‐mediated oxidations markers nitrotyrosine, nitrotryptophan, hydroxy‐tryptophan, and 3‐chlorotyrosine. Several site‐specific oxidations were identified in apo B‐100 after treatment of intact LDL particles with the oxidants. We hypothesize that these regions could be accessible to oxidant and critical for early events in atherosclerotic plaque deposition.
Autonomous decentralized stabilizing control of DC microgrid

It is of prime importance in DC microgrid to stabilize dynamic behaviors of Line Regulating Converters (LRC) that connects multiple distributed generators (DGs) and loads each other. To this end, it is effective to adjust duty ratio of LRCs properly because this countermeasure can save capital investment cost. Moreover, it is better to apply decentralized control method to realize the ubiquitous DC microgrid in which generators and loads are freely connected or disconnected because the centralized control approach normally needs precise parameter setting and recalculation is required when the system structure has been changed. Hence, in this paper, we proposed a new stabilization control strategy which can adjust the duty ratio in an autonomous decentralized manner. By observing time change of terminal voltage fluctuations, the LRCs will autonomously adjust their control gains in order to get the entire grid stable. The effectiveness of the proposed method was verified based on DC microgrid model consisting of 4 DGs and 3 loads.
A Model of Motivation Based on Empathy for AI-Driven Avatars in Virtual Worlds

This paper presents a model of motivation based on empathy for artificial intelligence (AI)-driven avatars in virtual worlds. The model is theoretically inspired, based on understanding and sharing other people’s intentions. The AI architecture presented is aimed at providing an avatar with the capabilities of motivational coaching in learning situations. The model consists of two general modules for understanding and sharing motivation; understanding consists of shaping a model of the world while sharing consists of deciding the best course of action and reaction in the virtual world. The AI architecture and its associated algorithms are presented. Future areas for research are defined, including practical applications of this architecture for serious games and activities in virtual world applications.
Cardinality estimation using sample views with quality assurance

Accurate cardinality estimation is critically important to high-quality query optimization. It is well known that conventional cardinality estimation based on histograms or similar statistics may produce extremely poor estimates in a variety of situations, for example, queries with complex predicates, correlation among columns, or predicates containing user-defined functions. In this paper, we propose a new, general cardinality estimation technique that combines random sampling and materialized view technology to produce accurate estimates even in these situations. As a major innovation, we exploit feedback information from query execution and process control techniques to assure that estimates remain statistically valid when the underlying data changes. Experimental results based on a prototype implementation in Microsoft SQL Server demonstrate the practicality of the approach and illustrate the dramatic effects improved cardinality estimates may have.
Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.
Probing The Constitutive Behaviour Of AnAl-Sn-Si Alloy By Tensile Testing AndInstrumented Indentation

Al-Sn-Si alloys used in tribological applications present a composite microstructure consisting of the Al-matrix, Sn-grains and dispersed Si and intermetallic particles. Upon plastic deformation, compatibility strains at the matrix-particle interface produce an initial stage of pronounced work hardening, while cold-rolled samples show partial recovery at room temperature which reduces the yield strength. Both phenomena lead to anomalous hardening. Ascast thin slabs of Al-13%Sn-2.5%Si-1%Cu were cold rolled and annealed at 300°C for 60 min. Tensile tests were performed along the rolling direction and micro-indentation tests executed along the normal, transverse and rolling direction. Tensile curves were fitted by several conventional hardening relations, of which the Voce-relationship provided the best flexibility in dealing with the non-standard hardening behaviour. Use of the fitted data in a finite-element model for microindentation provided a reasonable approximation to the measured load-displacement curves but material anisotropy is an overriding factor which was not accounted for. Evidence of creep was clearly present both in the loading stage as in the constant load part of the indentation curves.
Minimizing peak power consumption during scan testing: test pattern modification with X filling heuristics

Scan architectures, though widely used in modern designs, are expensive in power consumption. In this paper, we discuss the issues of excessive peak power consumption during scan testing. We show that taking care of high current levels during the test cycle (i.e. between launch and capture) is highly relevant to avoid noise phenomena such as IR-drop or ground bounce. We propose a solution based on power-aware assignment of don't care bits in deterministic test patterns. For ISCAS'89 and ITC'99 benchmark circuits, this approach reduces peak power during the test cycle up to 89% compared to a random filling solution
Progressive Failure Analysis of Cross-ply Laminated Composite Plates by Finite Element Method

The progressive failure analysis of laminated composite plates under transverse static loading has been carried out in the linear and elastic range. The laminated composite plate has been modeled using eight-noded isoparametric plate bending elements. The first-order shear deformation theory has been employed to account for the shear flexibility characteristics through the thickness direction of a composite laminate. A shear correction factor has been considered to reduce discrepancy between the assumed constant shear strain and the actual parabolic variation of the shear strain in the transverse direction. The mid surface of the laminate has been considered as the reference plane in the present formulation. After the failure of the weakest ply, the stiffness is reduced by either fiber failure or matrix failure. The stiffness of failed lamina has been totally discarded and other existing laminae are considered to remain unchanged after the weakest ply failure. The strength of the laminate at the same point is evaluated again to see if the laminate can carry additional load. This ply-by-ply analysis progresses until the ultimate strength of the laminate is reached. The results in terms of first-ply failure load obtained in the present investigation have been compared with those available in published literature. A parametric study has been carried out to estimate the influence of stacking sequence, fiber orientation, layer thickness, aspect ratio and the number of layers in the laminated composite plates on the prediction of ultimate failure load.
A Bayesian Reassessment of Nearest-Neighbor Classification

The k-nearest-neighbor (knn) procedure is a well-known deterministic method used in supervised classification. This article proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment found in Holmes and Adams, and evaluated by Manocha and Girolami, where the underlying probabilistic model is not completely well defined. Once provided with a clear probabilistic basis for the knn procedure, we derive computational tools for Bayesian inference on the parameters of the corresponding model. In particular, we assess the difficulties inherent to both pseudo-likelihood and path sampling approximations of an intractable normalizing constant. We implement a correct MCMC sampler based on perfect sampling. When perfect sampling is not available, we use instead a Gibbs sampling approximation. Illustrations of the performance of the corresponding Bayesian classifier are provided for benchmark datasets, demonstrating in particular the limitations of the pseudo-likelihood approximation in this set up.
The claustrum, the external capsule and the extreme capsule of Macaca mulatta

The claustrum is a sheet of gray matter situated between the putamen and the insular cortex. The external capsule borders the claustrum medially and separates it from the putamen. Interposed between the claustrum and the island is the extreme caps&. Xlost of the interest in the claustrum in past years has centered in ontogenetic and phylogenetic studies. No agreement has been reached amongst the various investigators as to its significance and much controversy still exists. At present, the knowledge of the fiber connections of the claustrum is incomplete. The ways in which the basal ganglia and the insular cortex are related to the claustrum and its surrounding capsulcs present interesting problems. One might speculate that the claustrum serves as :I relay in the discharge; directly or by way of the basal ganglia: from the supplementary motor areas of the cortex to the tegmc-ntum of the midbrain. The two capsules that bound the claustrum have also not been completely evaluated. There is no consensus regarding the functional significance or the fiber componeiits of the cxternal and the extreme capsules and the claustrum.
Specific circulating immune complexes in amoebic liver abscess

A micro enzyme-linked immunosorbent assay was used to detect the presence of amoebic antigen in polyethylene-glycol-precipitated circulating immune complexes (CIC). A cutoff dilution of greater than 1:160 for the precipitates was taken to be of clinical significance. Among the patients with amoebic liver abscess, 93% (14 of 15 confirmed cases) had indications of amoebic antigen in CIC. In 57% of the suspected cases of amoebic liver abscess, amoebic antigen could be detected in CIC. The specificity of the technique for the demonstration of specific amoebic antigen is indicated by the undetectable levels of amoebic antigen in CIC in serum samples from nine cases of nonamoebic hepatic diseases and 10 apparently healthy subjects. It is thus believed that the specific diagnosis of an individual case of amoebic liver abscess can be made by demonstrating specific amoebic antigen in CIC.
Socioeconomic Determinants of Poverty in Paraguay

To this day, poverty strikes about one in four Paraguayans. This study sought to explore the determinants of poverty in Paraguay and to find which household characteristics were related to poverty status. By finding these possible determinants of poverty, policymakers can design improved social policies that target poverty more effectively. To analyze the determinants of poverty both an ordinary least squares regression and logistic models were used with data from Paraguay's 2013 Permanent Survey of Households. Among the primary factors significantly related to poverty or lower incomes were being a female household head, being only Guarani-speaking, a lack of education, lacking health insurance, lacking access to sewers, lacking a work contract, being self-employed, and working in agriculture (for the general and urban data). Among the possible policy recommendations given were strengthening bilingual education programs, enhancing enrollment in, and access to, secondary and tertiary education, an agrarian reform for a more equitable land distribution, formalizing informal sector employment, improving access to health insurance, and improving access to sewers. By better understanding poverty, the Paraguayan government's policies will be more effective in their goals of reducing the high poverty rate in the nation.