Miracle-dz/UniFAR-FTUs huggingface dataset:
Socio-economic influences on anthropometric status in urban South African adolescents: sex differences in the Birth to Twenty Plus cohort

Abstract Objective To investigate the associations of household and neighbourhood socio-economic position (SEP) with indicators of both under- and overnutrition in adolescents and to explore sex differences. Design Analysis of anthropometric, household and neighbourhood SEP data from the Birth to Twenty Plus cohort born in 1990. Anthropometric outcomes were BMI (thinness, overweight and obesity) and percentage body fat (%BF; low, high). Associations between these and the household wealth index, caregiver education and neighbourhood SEP tertile measures were examined using binary logistic regression. Setting Johannesburg–Soweto, South Africa. Subjects Adolescents aged 17–19 years (n 2019; 48·2 % men). Results Women had a significantly higher combined prevalence of overweight/obesity (26·2 %) than men (8·2 %) whereas men had a significantly higher prevalence of thinness than women (22·2 % v. 10·6 %, respectively). Having a low neighbourhood social support index was associated with higher odds of high %BF in women (OR=1·59; 95 % CI 1·03, 2·44). A low household wealth index was associated with lower odds of both overweight (OR=0·31; 95 % CI 0·12, 0·76) and high %BF in men (OR=0·28; 95 % CI 0·10, 0·78). A low or middle household wealth index was associated with higher odds of being thin in men (OR=1·90; 95 % CI 1·09, 3·31 and OR=1·80; 95 % CI 1·03, 3·15, respectively). For women, a low household wealth index was associated with lower odds of being thin (OR=0·49; 95 % CI 0·25, 0·96). Conclusions The study highlights that even within a relatively small urban area the nutrition transition manifests itself differently in men and women and across SEP indicators. Understanding the challenges for different sexes at different ages is vital in helping to plan public health services.
Research on Similarity Mining for Flight Data

Similarity search for time series has attracted much research attention recently. Flight Data is a set of data arranged by chronological order. Through depth study on the characteristics of Flight Data, in this paper a subsequence matching algorithm based on the Discrete Fourier Transform (DFT) for Flight Data is proposed. In order to reduce the number of dimensions for Flight Data, this algorithm uses the Discrete Fourier Transform (DFT) to map the original Flight Data to the frequency domain. Then, the spatial data index structure is built using the adaptive segmentation, and the Euclidean distance is used as the similarity measurement. The experiment results demonstrate that this algorithm has much higher time efficiency and accuracy rate than the sliding window regression algorithm.
Design and analysis of an RLS-type modified filtered-x algorithm for adaptive IIR filters

This study presents design and robust stability analysis of a novel version of RLS-type adaptive IIR filter in the modified filtered-x structure. The derivation of the algorithm is by transforming the original ANVC problem to an output-error identification problem without assuming that the slow adaptation condition holds. By considering fast adaptation of the filter weights and also the assumption that nonparametric uncertainty exists in the estimation of the secondary path, the stability of the proposed algorithm is analyzed using Lyapunov theory. In fact by introducing a time-varying scalar parameter in the adaptation, a sufficient condition based on the value of this parameter and the size of the uncertainty is derived.
Digitized by the Internet Archive in 2011 with Funding from a New Keynesian Model with Unemployment Libraries a New Keynesian Model with Uneraployment* We Thank

We develop a utility based model of fluctuations, with nominal rigidities, and unemployment. In doing so, we combine two strands of research: the New Keynesian model with its focus on nominal rigidities, and the Diamond-MortensenPissarides model, with its focus on labor market frictions and unemployment. In developing this model, we proceed in two steps. We first leave nominal rigidities aside. We show that, under a standard utility specification, productivity shocks have no effect on unemployment in the constrained efficient allocation. We then focus on the implications of alternative real wage setting mechanisms for fluctuations in unemployment. We then introduce nominal rigidities in the form of staggered price setting by firms. We derive the relation between inflation and unemployment and discuss how it is influenced by the presence of real wage rigidities. We show the nature of the tradeoff between inflation and unemployment stabilization, and we draw the implications for optimal monetary policy. JEL Classification: E32, E50.
INTER-ORGANIZATIONAL LEARNING DYNAMICS : ROLES OF NETWORKS ON KNOWLEDGE CREATION

The focus of most of the current literature on inter-organizational learning has been on how a firm acquires knowledge from its alliance partners. While knowledge acquisition is an important process, a firm may not effectively learn through such process, especially when the knowledge is complex, tacit, and embedded. Further, despite the increasing importance of networks, current research on inter-organizational learning has been limited to dyadic relations. To address these issues, this paper adopts a network perspective and develops a conceptual framework by arguing that inter-organizational learning occurs through both knowledge acquisition and knowledge development processes and that different network characteristic facilitate knowledge creation through these processes. Further, it suggests that absorptive capacity moderates the relationships between the properties of the network and effectiveness of the two learning processes in creating knowledge.
SENTENCE LEVEL TEXT CLUSTERING USING A FUZZY HIERARCHICAL RELATIONAL CLUSTERING ALGORITHM SureshKumar

Sentence clustering plays an important role in many text processing activities. For example, various authors have argued that incorporating sentence clustering into extractive multi document summarization helps avoid problems of content overlap, leading to better coverage. Sentence clustering is important to cluster the sentence which is likely to be related to more than one theme or topic present within a document or set of documents. In existing system a novel fuzzy clustering algorithm is used that operates on relational input data. The algorithm uses a graph representation of the data, and operates in an Expectation-Maximization framework in which the graph centrality of an object in the graph is interpreted as a likelihood. Results of applying the algorithm to sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping clusters of semantically related sentences, and that it is therefore of potential use in a variety of text mining tasks. Proposed system improve the result of the clustering by applying fuzzy theory into hierarchical clustering method. After datasets were divided into several sub-clusters using partitioning method, fuzzy graph of sub-clusters was constructed by analyzing the linked fuzzy degree among the sub-clusters. By making a cut graph for the fuzzy graph, the connected components of the fuzzy graph is determined, which were the result of clustering. The results of experimental study in data sets with arbitrary shape and size are very encouraging. The experimental study help us to efficiently clustering the sentence level text. KeyTerms—2D-to-3D-conversion, depth image-based rendering, Disparity map based rendering, hole-filling, image warping, optimization.
Population Aging and Economic Growth: the effect of health expenditure

Rising longevity has led to population aging in developed countries, causing increasing concerns about its economic impact. Specially, the trend of population aging increases health expenditure in developed countries, and 70% to 80% of health expenditure is funded by public sector. Therefore, this paper focuses on the health demand in an aging economy and examines how the aging of the population and public health funding (PHF) affects agents' behavior. For this purpose, we construct a simple growth model and examine the e®ect of aging and PHF on saving and the growth rate. We show that an increase in life expectancy increases the growth rate in the economy without PHF, but that it has an inverted U-shaped relation in the economy with PHF. From the welfare standpoint, we show that it increases the intergenerational conflict between current and future generation and that PHF has the result of alleviating the conflict.
Multiple hypothesis tracking with multiframe assignment using range and range-rate measurements

In this paper we consider the problem of multitarget tracking using range and range-rate measurements collected from a single maneuvering platform. Although bearings-only tracking has been studied extensively, only a few studies exist in the literature that consider range-only tracking. The motivating application for this work is maritime surveillance using radars equipped with inverse synthetic aperture radar mode. We consider a realistic scenario that consists of an unknown number of targets with measurement origin uncertainty due to their presence and false alarms, and imperfect radar measurements. The algorithm that we proposed is a multiple hypothesis tracker based on the multiframe assignment algorithm. Different track initialization techniques depending on whether or not initial bearing measurements are available are considered and an angle-parameterized filter is used to track the targets. Simulations conducted show the effectiveness of the proposed algorithm in terms of tracking accuracy and track maintenance capability.
Improving the representation of hydrologic processes in Earth System Models

Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large‐scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater‐surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles.
Word Alignment-Based Reordering of Source Chunks in PB-SMT

Reordering poses a big challenge in statistical machine translation between distant language pairs. The paper presents how reordering between distant language pairs can be handled efficiently in phrase-based statistical machine translation. The problem of reordering between distant languages has been approached with prior reordering of the source text at chunk level to simulate the target language ordering. Prior reordering of the source chunks is performed in the present work by following the target word order suggested by word alignment. The testset is reordered using monolingual MT trained on source and reordered source. This approach of prior reordering of the source chunks was compared with pre-ordering of source words based on word alignments and the traditional approach of prior source reordering based on language-pair specific reordering rules. The effects of these reordering approaches were studied on an English--Bengali translation task, a language pair with different word order. From the experimental results it was found that word alignment based reordering of the source chunks is more effective than the other reordering approaches, and it produces statistically significant improvements over the baseline system on BLEU. On manual inspection we found significant improvements in terms of word alignments.
Incidence of secondary malignancies among patients with Waldenström macroglobulinemia: An analysis of the SEER database

Waldenström macroglobulinemia (WM) is an indolent malignancy that predominantly affects older individuals who are at risk for secondary malignancies (SMs). The objective of this study was to characterize the incidence of SMs after a diagnosis of WM with the Surveillance, Epidemiology, and End Results (SEER) database.
Hardware Mechanisms for Distributed Dynamic Software Analysis

The complexity of modern software makes it difficult to ship correct programs. Errors can cost money and lives, so developers often use automated software analysis tools to hunt for bugs. By automatically analyzing a program's runtime operation, dynamic analysis tools can find subtle errors that would normally escape the notice of even careful human testers. These dynamic analyses are more likely to find errors if they observe the program under numerous and varied runtime situations, so, ideally, users would analyze programs as they run them. Regrettably, these tests cause orders-of-magnitude slowdowns, which few users would tolerate. This dissertation presents methods of accelerating dynamic software analyses in order to distribute these trials to users. It begins by observing that the overhead that any individual observes can be lowered by allowing each user to analyze a small, random sample of the program's operations. It then describes a sampling mechanism for dynamic dataflow analyses that only requires access to the virtual memory system. Other dynamic software analyses cannot use this technique, so the remainder of this dissertation focuses on novel hardware-based acceleration mechanisms. The first, Testudo, allows faster and more accurate dataflow analysis sampling by storing meta-data in an on-chip cache and stochastically removing these values whenever it overflows. Next, a method for performing demand-driven data race detection using existing hardware performance counters is detailed. These counters make inter-cache sharing events visible to software, allowing the data race detector to be enabled only when needed. Finally, fine-grained memory monitoring is a crucial for every analysis studied in this work. This dissertation closes by examining a hardware mechanism for allowing unlimited fine-grained watchpoints, which can be used to reduce the overheads of many analyses. In total, this dissertation demonstrates that hardware designers can play a role in mitigating software bugs, as many of the acceleration mechanisms explored in this work require hardware support in one form or another. Taken together, these works represent a step forward for accelerating dynamic software analyses and distributing them to end-users.
A FUZZY REASONING SYSTEM FOR BINOCULAR SCENE MATCHING

A new method to solve the binocular correspondence problem[1] is developed. It is based on the integration of fuzzy systems [3] and heuristic tree-search theories[4]. Fuzzy logic is used to obtain a measure of similarity between a pair of tokens, through a reasoning process which emulates that of a human observer. A tree-search strategy is used for the task of generating the set of potential pairs required to find the best matching of all tokens in the scene. An illustrative computational point-based stereo matching system has been implemented and its performance evaluated.
Effects of Dietary Supplementation of Oregano Essential Oil to Sows on Oxidative Stress Status, Lactation Feed Intake of Sows, and Piglet Performance

Fifty-four multiparous large white sows were used to determine the effects of supplementing oregano essential oil (OEO) to the gestation and lactation diets on oxidative stress status, lactation feed intake, and their piglet performance. Two groups were fed diets with (OEO; n = 28) or without (Control; n = 26) supplemental 15 mg/kg OEO during gestation and lactation. The serum levels of reactive oxygen species (ROS) (P < 0.05), 8-hydroxy-deoxyguanosine (8-OHdG) (P < 0.05), and thiobarbituric acid reactive substances (TBARS) (P < 0.05) were higher during gestation (days 90 and 109) and lactation (days 1 and 3) than in early gestation (day 10). Compared with the control group, the OEO diet significantly reduced sows' serum concentrations of 8-OHdG (P < 0.05) and TBARS (P < 0.01) on day 1 of lactation. The OEO diet increased the sows' counts of faecal lactobacillus (P < 0.001) while reducing Escherichia coli (P < 0.001) and Enterococcus (P < 0.001). In the third week of lactation the treatment tended to increase sow's feed intake (P = 0.07), which resulted in higher average daily gain (P < 0.01) of piglets. Our results demonstrated that there is an increased systemic oxidative stress during late gestation and early lactation of sows. The OEO supplementation to sows' diet improved performance of their piglets, which may be attributed to the reduced oxidative stress.
Continuous Jetting of Alginate Microfiber in Atmosphere Based on a Microfluidic Chip

We present a method based on a microfluidic chip that produces continuous jetting of alginate microfiber in the atmosphere to facilitate its collection and assembly. Through the analysis of the factors influencing the microfiber jetting, the principle and some microfluidic chip design criteria are discussed. A special nozzle is designed near the chip outlet, and deionized water is introduced into the microchannel through the nozzle to increase the flux and thus to prevent drop formation around the outlet which impedes the continuous jetting of microfiber. The experiments have reported the effectiveness of the proposed structure and shown that the introduction of sheath flow promotes the stability of the flow field in the microchannel and does not affect the morphology of microfiber. Simulations of velocity and pressure distribution in the microchannel are also conducted. Further, the jetting microfibers are collected and assembled into various 3D complex fiber-based macroscopic structures through patterning or reeling. Since the proposed structure is rather simple and can be easily integrated into other complex structures without adding more soft-lithographical steps, microfibers with various morphology and function can be synthesized and collected in a single chip, which can be applied to various fields, such as tissue engineering, biotechnology, and drug discovery.
Direct primary receiver detection with frequency mapping in cognitive radio

A cognitive radio should avoid interference from secondary users to primary users. However, the unknown location of primary receiver makes the problem hard to solve. This paper proposes a novel primary receiver detection to change the passive state of primary receivers. Primary receiver informs the secondary user it's licensed receive frequency according to semaphore architecture via mapping method. The secondary user determines the spectrum holes by the semaphore. The algorithm of the mapping method and the semaphore transmitting power control are proposed. The analysis on the simulation results shows that the scheme can realize the detection of primary receiver, avoid interference and improve the spectrum utilization effectively.
Study of Particulate Matter Formation and Evolution in Near-Field Aircraft Plumes using a One-Dimensional Microphysical Model

Environmental concerns have led to a growing effort to investigate and characterize the particulate matter (PM) emissions from aircraft engines. This thesis presents a onedimensional microphysics and chemical kinetics model that is used to study the formation and evolution of particulate matter in near field aircraft plumes at ground level. The initial exhaust properties were obtained from engine data and plume mixing profiles were generated using FLUENT*. The initial gas species concentrations were estimated based on a two-step constrained equilibrium process. Parametric studies of the effects of ambient temperature, ambient relative humidity, engine operating conditions and engine design parameters on the aerosol formation were investigated using the one dimensional model following a centerline trajectory up to lkm downstream of the engine exit plane. PM formation and evolution characteristics along trajectories from several radial locations were also investigated and compared to the results from the centerline study. The results from this study show that binary homogeneous H2SO 4-H 20 nucleation strongly depends on ambient conditions such as ambient temperature and relative humidity whereas the condensational growth of soot particles is most dependent on engine power settings. For example, the nucleation mode sulfur emissions index varies from 0.01 mg/kg-fuel at 299 K and 80% relative humidity to 9 mg/kg-fuel for the same level of relative humidity at 288K. The nucleation mode sulfur Elm increased from 0.01 mg/kg-fuel at 10% relative humidity and 7% engine power setting to about 9 mg/kg-fuel at 80% relative humidity for the same power setting and ambient temperature. The amount of SvI condensed on soot particles changes from around 2 mg/kgfuel to around 8 mg/kg-fuel as the engine power increases from 7% to 100% for the same 10% relative humidity. Engine design parameters such as engine bypass ratio as well as core and bypass total temperature also play important roles in both the homogenous nucleation and heterogeneous condensation processes in the plume. It was found that at 80% relative humidity and 100% power setting, the sulfur emission index increased from 1.8 mg/kg-fuel at the high BPR of 9 to a value of 2.5 mg/kg-fuel for the limiting case of zero BPR. The sulfur emission index for the nucleation mode increased from around 0.7 mg/kg-fuel at the higher core and bypass total temperature (15 K above the CFM56-2C1 values) to 3.5 mg/kgii fuel at the lower temperature (15 K below the CFM56-2C1 engine values). The study sheds light on the PM formation mechanisms in near field aircraft plumes and provides guidance on the improvement of future experimental measurement techniques.
Workload factors impacting managers

Purpose – The paper seeks to identify elements of lean production that may be applicable to managers.Design/methodology/approach – A survey was administered to 125 middle and lower level managers to assess perceptions about level of workload and amount of workload control. Principal components analysis was employed to identify primary factors.Findings – Five primary factors accounted for 56 percent of total variance. The principal factors corresponded to the lean production concepts of work in process level, empowerment, and non value‐added time.Originality/value – These middle and lower level managers identified five primary factors that accounted for 56 percent of the variance in their self‐reported levels of workload and control. Three of these factors correspond to existing metrics that have been utilized in lean production in industry.