Miracle-dz/UniFAR-FTUs huggingface dataset:
Interdependence of Stress Processes Among African American Family Members: Influence of HIV Serostatus and a New Infant

This study makes a theoretical contribution to stress process research by using a systemic approach to contextualize individual outcomes within the framework of other family members' experience. Utilizing a mixed model approach, indicators of the stress process of urban low-income HIV + African American recent mothers were found to affect the psychological distress and perceived adequacy of coping of multiple other family members. These relationships were found to be strongest proximal to birth and to be exacerbated by HIV infection. Social support to the mother was found to have differential effects depending on whether it was from the immediate family or outside sources. HIV infection of the recent mother was found to affect family members both through relationships of the mother's stress process and through their own coping responses.
Path Dependence and New Technological Path Creation in the Danish Wind Power Industry

In this paper, it is argued that in order to overcome the limitations of canonical path dependence and new path creation theories that arise from the different ontological approaches of economics and sociology, a hybrid socio-economic theory is required that examines the contributions of intelligent agents to the creation of new technological pathways in conditions of path dependence. The main aim of such a theory is to explain the processes by which agents may collectively contribute to the emergence of new technological pathways and overcome the barriers confronting them as a result of the evolution of historical forces that establish the path-dependent trajectories of contemporary technologies. The arguments are illustrated empirically by analysing the roles of inventors and innovation pioneers located in economic niches together with the diffusion of new technologies to the attainment of critical mass and the creation of new pathways. The roles and processes engaged in by such actors, and the path-dependent barriers confronting them, are illustrated using the historical creation the renewable energy technological pathway in wind power in Denmark.
Ultrastructural localization of GPR179 and the impact of mutant forms on retinal function in CSNB1 patients and a mouse model.

PURPOSE Complete congenital stationary night blindness (CSNB1) is characterized by loss of night vision due to a defect in the retinal ON-bipolar cells (BCs). Mutations in GPR179, encoding the G-protein-coupled receptor 179, have been found in CSNB1 patients. In the mouse, GPR179 is localized to the tips of ON-BC dendrites. In this study we determined the ultrastructural localization of GPR179 in human retina and determined the functional consequences of mutations in GPR179 in patients and mice. METHODS The localization of GRP179 was analyzed in postmortem human retinas with immunohistochemistry. The functional consequences of the loss of GPR179 were analyzed with standard and 15-Hz flicker ERG protocols. RESULTS In the human retina, GPR179 is localized on the tips of ON-BC dendrites, which invaginate photoreceptors and terminate juxtaposed to the synaptic ribbon. The 15-Hz flicker ERG abnormalities found in patients with mutations in GPR179 more closely resemble those from patients with mutations in either TRPM1 or NYX than in GRM6. 15-Hz flicker ERG abnormalities of Gpr179(nob5) and Grm6(nob3) mice were comparable. CONCLUSIONS GRP179 is expressed on dendrites of ON-BCs, indicating that GRP179 is involved in the ON-BCs' signaling cascade. The similarities of 15-Hz flicker ERGs noted in GPR179 patients and NYX or TRPM1 patients suggest that the loss of GPR179 leads to the loss or closure of TRPM1 channels. The difference between the 15-Hz flicker ERGs of mice and humans indicates the presence of important species differences in the retinal activity that this signal represents.
Bursting Bubbles: Linking Experimental Financial Market Results to Field Market Data

The laboratory, where variables can be measured and controlled, is perhaps the most efficient place to test scientific theory, yet in general empirical financial research neglects experimental finance results. We link laboratory findings to actual, or field, data, applying the Smith, Suchanek and Williams [1988] experimental market model to Australian stock exchange data. We introduce modifications that improve the model's fit to field market conditions. The experimental model is a reliable predictor of field market bubble bursts in more than 50% of the cases we test, and our modifications improve the performance to 77% of the cases. Our results suggest that experimental financial market results should be accorded more attention in empirical research.
New Evidence On Hedge Fund Performance Measures

Hedge funds are still relatively unfamiliar to most investors despite the intense popularity they have enjoyed in recent years. Measuring the performance of these financial instruments using traditional methods is, however, problematic, since their returns do not follow a normal distribution. In this study, we consider rankings obtained with the Stochastic Dominance (SD) method and compare them with ranks produced using Sharpe Ratios, Modified Sharpe Ratios, and Data Envelopment Analysis. We also explore the advantages highlighted by the literature of the Data Envelopment Analysis (DEA) method in relation to traditional measures like Sharpe ratio and Modified Sharpe ratio. Our results show that classic performance measures are better correlated with SD than DEA results.
Algorithms for reliable peer-to-peer networks

This thesis addresses the problem of the lack of reliability in peer-to-peer networks, and proposes a number of algorithms that can provide reliability guarantees to peer-to-peer applications. Note that reliability in a peer-to-peer networking context is different from TCP type reliability. We define a reliable peer-to-peer as a network that is resilient to changes such as network dynamics, and can offer participating peers increased performance when possible. We make the following contributions to area of peer-to-peer reliability: (1) we propose an algorithm that creates resilient low-diameter topologies that guarantee an upper bound on delays among nodes; (2) we study parallel downloads in peer-to-peer networks and how they affect nodes by looking at their utilities and the overall performance of the network; and (3) we investigate network metrics relevant to peer-to-peer networks and their estimation using incomplete information. While we focus on latency and hop count as drivers for improving the performance of the peers, the proposed approach is more generally applicable to other network-wide metrics (e.g., bandwidth, loss). Our research methodology encompasses simulations and analytical analysis to understand the behavior and properties of the proposed systems, and substantial experimentation, as practical proof of concept of our ideas, using the PlanetLab platform. The common overarching theme of the thesis is the design of new resilient network algorithms capable of offering high-performance to peers and their applications. As more and more applications rely on underlying peer-to-peer topologies, the need for efficient and resilient infrastructure has become more pressing. A number of important classes of topologies have emerged over the last several years, all of which have various strengths and weaknesses. For example, the popular structured peer-to-peer topologies based on Distributed Hash Tables (DHTs) offer applications assured performance, but are not resilient to attacks and major disruptions that are likely in the overlay. In contrast, unstructured topologies where nodes create random connections among themselves on-the-fly, are resilient to attacks but can not offer performance assurances because they often create overlays with large diameters, making some nodes practically unreachable. In our first contribution, we propose Phenix, an algorithm for building resilient low-diameter peer-to-peer topologies that can resist different types of organized and targeted malicious behavior. (Abstract shortened by UMI.)
Neural Network with Forgetting: An ANN Algorithm for Customer

As a useful analytical tool, artificial neural networks (ANN) are widely applied in analyzing the information stored in enterprise database or data warehouse. Certain structured ANN can also be used for customer segmentation. In the process of establishing the training data sets, there is the problem of selecting customer data from potentially wide-varying time periods, as is in a typical enterprise data warehouse. To solve this problem, we introduced a forgetting coefficient into the BP ANN; we also established a new ANN, ANN with forgetting, to improve the customer segmentation process.
Policy transfer in critical perspective

The world of public policy is becoming increasingly small due to dramatic changes in global communications, political and economic institutional structures, and to nation states themselves. This article evaluates the implications of these changes and challenges for both the study and the practice of policy transfer and provides an understanding of the relationship between systemic globalizing forces and the increasing scope and intensity of policy transfer activity. It provides: an explanation of policy transfer as a process of organizational learning; an insight into how and why such processes are studied by policy scientists; and an evaluation of its use by policy practitioners. The article argues that the limits of policy transfer analysis as a descriptive, explanatory and prescriptive theory of policy change can be addressed through the development of a multi-level ‘action based’ approach to the study of policy transfer.
Accounting and non‐accounting measures of quality‐based performances in small firms

In recent years it has been often claimed that quality is one of most critical success factors for organizations. Managers introduced quality‐based programmes – such as total quality management – assuming that performances would improve. However, many quality‐based initiatives failed. There are several reasons that could explain the failure of quality‐based strategies in a number of firms; suggests two causes: the lack of effective decisional tools for evaluating the most effective investment(s) among a set of potential programmes; and the lack of specific goals to be assigned to each investment in order to monitor the actual results of the programmes on time. In small firms these problems are greater because of the limited availability of financial and managerial resources which make more difficult the identification of the most effective decisional solutions. Identifies a conceptual framework aimed at supporting the choice of most effective models for evaluating quality‐related investments in small firm.
The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores

Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.
Unsupervised Primary Object Discovery in Videos Based on Evolutionary Primary Object Modeling With Reliable Object Proposals

A novel primary object discovery (POD) algorithm, which uses reliable object proposals while exploiting the recurrence property of a primary object in a video sequence, is proposed in this paper. First, we generate both color-based and motion-based object proposals in each frame, and extract the feature of each proposal using the random walk with restart simulation. Next, we estimate the foreground confidence for each proposal to remove unreliable proposals. By superposing the features of the remaining reliable proposals, we construct the primary object models. To this end, we develop the evolutionary primary object modeling technique, which exploits the recurrence property of the primary object. Then, using the primary object models, we choose the main proposal in each frame and find the location of the primary object by merging the main proposal with candidate proposals selectively. Finally, we refine the discovered bounding boxes by exploiting temporal correlations of the recurring primary object. Extensive experimental results demonstrate that the proposed POD algorithm significantly outperforms conventional algorithms.
evelopment of a solid-phase microextraction gas hromatography with microelectron-capture detection ethod for a multiresidue analysis of pesticides n bovine milk

A simple and rapid method based on solid-phase microextraction (SPME) technique followed by gas chromatography with microelectron-capture detection (GCECD) was developed for the simultaneous determination of more than 30 pesticides (pyrethroids and organochlorinated among others) in milk. To our knowledge, this is the first application of SPME for the determination of pyrethroid pesticides in milk. Negative matrix effects due to the complexity and lipophility of the studied matrix were reduced by diluting the sample with distilled water. A 25–1 fractional factorial design was performed to assess the influence of several factors (type of fiber coating, sampling mode, stirring, extraction temperature, and addition of sodium chloride) on the SPME procedure and to determine the optimal extraction conditions. After optimization of all the significant variables and interactions, the recommended procedure was established as follows: DSPME (using a polydimethylsiloxane (PDMS)/divinylbenzene (DVB) coating) of 1 mL of milk sample diluted with Milli-Q water (1:10 dilution ratio), at 100 ◦C, under stirring for 30 min. The proposed method showed good linearity and high sensitivity, with limits of detection (LOD) at the sub-ng mL−1 level. Within a day and among days precisions were also evaluated (R.S.D. < 15%). One of the most important attainments of this work was the use of external calibration with milk-matched standards to quantify the levels of the target analytes. The method was tested with liquid and powdered milk samples with different fat contents covering the whole commercial range. The efficiency of the extraction process was studied at several analyte concentration levels obtaining high recoveries (>80% in most cases) for different types of full-fat milks. The optimized procedure was validated with powdered milk certified reference material, © 2008 Elsevier B.V. All rights reserved. ∗ Corresponding author. Tel. : +34 981 563100; fax: +34 981 595012. E-mail address: qblvrlgb@usc.es (M. Llompart). 003-2670/$ – see front matter © 2008 Elsevier B.V. All rights reserved. oi:10.1016/j.aca. 2008.01. 021 38 a n a l y t i c a c h i m i c a a c t a 6 1 7 ( 2 0 0 8 ) 37–50 which was quantified using external calibration and standard addition protocols. Finally, the DSPME-GCECD methodology was applied to the analysis of milk samples collected in from farms of dairy cattle
R-Peak detection in ECG signal compression for Heartbeat rate patients at 1 KHz using High Order Statistic Algorithm

One of the major problems in the analysis of Electro-Cardiogram (ECG) signal compression is the accurate detection of R-peak. It is due to the difficulties varied by the time varying morphology of ECG, the physiological variations due to the patient and the noise contamination. However, it includes power line interface, muscle contraction noise, poor electrode contact, patient movement, and baseline wandering due to respiration. R-peak applications require accurate heart beat monitoring systems including intensive care units, operating rooms, implantable pacemakers and defibrillators. Moreover, it detects a QRS complex when ECG amplitude exceeds a threshold level. If the threshold is too high, true beats can be missed. If the threshold is too low, false detection can result during EMG artifact and external interface. So, during these artifacts, the magnitude of the noise can become larger than the signal, QRS detection based on amplitude threshold alone is not satisfactory. The problem was due to the detection threshold used, not the matched filter implementation. The R peak detection threshold was two-thirds of the average value of the output of the matched filter for the specific feature of interest. To overcome this problem, R peak detection has been proposed because it is adaptive to the nonlinear and time varying features of ECG signal. It can be trained to recognize the normal waveform and filter out the unnecessary artifacts and noises. Usually R-peak detection in QRS complex can be improved by considering multiple features, including RR interval, pulse duration and amplitude. In this research paper, is to take the difference of maximum original signal and minimum original signal to obtain the filtered R-peak ECG signal after 1 st and 2 nd pass to observe noise for 512 sample points at sampling frequency of 1 KHz using High Order Statistics Algorithm.Once the Rpeak is detected, compute the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). From analytical perspective of this research, it appears to be exceedingly robust, correctly detects R-peaks even aberrant QRS complexes in noise-corrupted ECG signal compression. Keywords—component; ECG, R-Peak, QRS, FFT, High Order Statistics Algorithm, Threshold, Skewness, Kurtosis, Power Spectrum Density,
Rolling stylometry

This paper introduces a new stylometric method that combines supervised machine-learning classification with the idea of sequential analysis. Unlike standard procedures, aimed at assessing style differentiation between discrete text samples, the new method, supported with compact visualization, tries to look inside a text represented as a set of linearly sliced chunks, in order to test their stylistic consistency. Three flavors of the method have been introduced: (i) Rolling SVM, relying on the support vector machines classifier, (ii) Rolling NSC, based on the nearest shrunken centroids method, and (iii) Rolling Delta, using the classic Burrowsian measure of similarity. The technique is primarily intended to assess mixed authorship; however, it can be also used as a magnifying glass to inspect works with unclear stylometric signal. To demonstrate its applicability, three different examples of collaborative work have been briefly discussed: (i) the 13th-century French allegorical poem Roman de la Rose, (ii) a 15th-century translation of the Bible into Polish known as Queen Sophia’s Bible, and (iii) The Inheritors, a novel collaboratively written by Joseph Conrad and Ford Madox Ford in 1901.
Pricing Interest Rate Derivatives under Multi-Factor GARCH

This paper presents semi-closed-form solutions to a wide range of interest rate derivatives, such as options on discount bonds and on coupon bonds, options on the short rate, options on yield spreads and on a basket of yields. A multi-factor GARCH framework of the short rate and its variance components is considered. We define a generalized zero-coupon bond and derive the moment generating function (MGF) of the discount bond log-price. The solution method relies on Fourier-inverting the MGF to compute the cumulative probabilities. The solution is found very accurate and offers considerable savings in computation time when compared to Monte Carlo simulation.
Slc12a2 is a direct target of two closely related homeobox proteins, Six1 and Six4

Six genes are homologs of Drosophila sine oculis and encode transcription factors that are characterized by a conserved Six domain and homeodomain. Of the six family members (Six1–Six6) in mice, Six1 and Six4 show similar expression patterns during embryogenesis. Six1–/– mice show defective formation of various organs such as inner ear, nose, skeletal muscle, kidney and thymus, whereas Six4–/– mice show little anomaly in organogenesis. To understand the molecular basis for the differential function of Six1 and Six4 in vivo, we screened target genes of Six1 and Six4 and found that Six1 and Six4 differentially regulated a set of target genes. Gel‐retardation assays indicated that the promoter region of one of the targets, sodium–potassium–chloride cotransporter 1 (Slc12a2), contains multiple Six1‐binding sites and one common binding site of Six1 and Six4, suggesting that the DNA‐binding specificity of Six1 is distinct from that of Six4. This underlies the differential regulation of common target genes by Six1 and Six4. Furthermore, in situ hybridization demonstrated that the expression of Slc12a2 was reduced in the developing dorsal root ganglia of Six1–/–/Six4–/– mice, suggesting that Six1 and Six4 regulate Slc12a2 in vivo.
Regional Anesthesia and Pain Management for the Pediatric Patient

1. Describe the indications for regional anesthesia in the pediatric patient, as well as appropriate patient selection criteria. 2. Enumerate the benefits of regional anesthesia for pain management, specific to the pediatric outpatient population. 3. Review the recent advances that have propelled regional anesthesia into everyday use including novel techniques, improved equipment, and refined anesthetic drugs. 4. Describe potential risks, complications, and management of regional anesthesia in children.
The Trade-Off Between Child Labor and Schooling: Influence of Social Labeling NGOs in Nepal

Does the labeling of products which have been produced without any child laborers contribute to increased welfare of children? This paper presents some results of a survey in Nepal conducted to analyze which factors determine the probability of a child to work, and to examine the influence of non governmental organizations (NGOs) which are engaged in social labeling, on the incidence of child labor and child schooling. Data were obtained from interviews with 410 households of Kathmandu Valley in Nepal. The results of the econometric analysis show that the probability of child labor (i) decreases if the carpet industry has implemented a labeling program, (ii) decreases if the adult’s income increases (‘luxury axiom’), (iii) decreases if the head of the household is educated, (iv) increases with the age of the head of the household, and (v) is increased in the presence of more children (aged 5-14) in the household. It can also be shown that labeling NGOs have a significant positive influence on sending the ex-child laborers to school.