Miracle-dz/UniFAR-FTUs huggingface dataset:
Violence against pregnant women: prevalence and associated factors.

OBJECTIVE To identify the factors associated with domestic violence against pregnant women. METHODS Interviews were conducted with 1,379 pregnant women undergoing antenatal care in basic health care units of the Brazilian Health System, within the municipality of Campinas (Southeastern Brazil). A structured questionnaire on domestic violence, validated in Brazil, was applied between July 2004 and July 2006. The first and second interviews in a cohort study were analyzed. Descriptive and multiple logistic regression analysis of the data were conducted. RESULTS Psychological violence was reported by 19.1% (n=263) of the total sample of pregnant women and physical/sexual violence was reported by 6.5% (n=89) of them. The factors associated to psychological violence were: adolescent intimate partner (p<0.019) and the pregnant woman had witnessed physical aggression before she was 15 years old (p<0.001). The factors associated to physical/sexual violence were: difficulties encountered by the pregnant woman in attending her antenatal appointments (p<0.014), intimate partner uses drugs (p<0.015) and does not work (p<0.048). The factors associated to psychological and physical/ sexual violence were: low level of education of the interviewee (p<0.013 and p<0.020, respectively), the pregnant woman being responsible for the family (p<0.001 and p=0.017, respectively) pregnant woman had suffered physical aggression during childhood (p<0.029 and p<0.038, respectively), presence of common mental disorder (p<0.001) and intimate partner consumes alcoholic beverage twice or more weekly. (p<0.001). CONCLUSIONS A high prevalence of different categories of domestic violence by an intimate partner during pregnancy was found as well as different factors associated with them. Appropriate mechanisms are necessary, particularly in primary health care, to identify and deal with domestic violence during pregnancy.
Fast Distributed Well Connected Dominating Sets for Ad Hoc Networks

We present the first distributed algorithms for computing connected dominating sets (CDS) for ad hoc networks that break the linear-time barrier. We present two algorithms which require O(∆ log n) and O(log n) running time respectively, where ∆ is the maximum node degree and n is the size of the network. This is a substantial improvement over existing implementations, all of which require Ω(n) running time. A basic primitive which underlies our first algorithm is Distance-2 coloring of the network. This primitive arises naturally in many applications such as broadcast scheduling and channel assignment. Minimizing the number of colors used in the coloring is very desirable for these applications, but this is known to be NP-hard. We present a fast distributed implementation of D2-coloring for ad hoc networks which is guaranteed to use at most O(1) times the number of colors required by the optimal algorithm. Our algorithms are geared towards constructing Well Connected Dominating Sets (WCDS) which have certain powerful and useful structural properties such as low size, low stretch and low degree. In this work, we also explore the rich connections between WCDS and routing in ad hoc networks. Specifically, we combine the properties of WCDS with other ideas to obtain the following interesting applications: • An online distributed algorithm for collision-free, low latency, low redundancy and high throughput broadcasting. • Distributed capacity preserving backbones for unicast routing and scheduling. Throughout this work, our focus is on obtaining distributed algorithms for ad hoc wireless networks with provably good analytical performance guarantees, both in terms of the running times of the algorithms as well as the performance of the broadcast and unicast routing and scheduling applications
ZBTB17 (MIZ1) Is Important for the Cardiac Stress Response and a Novel Candidate Gene for Cardiomyopathy and Heart Failure

Background—Mutations in sarcomeric and cytoskeletal proteins are a major cause of hereditary cardiomyopathies, but our knowledge remains incomplete as to how the genetic defects execute their effects. Methods and Results—We used cysteine and glycine-rich protein 3, a known cardiomyopathy gene, in a yeast 2-hybrid screen and identified zinc-finger and BTB domain-containing protein 17 (ZBTB17) as a novel interacting partner. ZBTB17 is a transcription factor that contains the peak association signal (rs10927875) at the replicated 1p36 cardiomyopathy locus. ZBTB17 expression protected cardiac myocytes from apoptosis in vitro and in a mouse model with cardiac myocyte–specific deletion of Zbtb17, which develops cardiomyopathy and fibrosis after biomechanical stress. ZBTB17 also regulated cardiac myocyte hypertrophy in vitro and in vivo in a calcineurin-dependent manner. Conclusions—We revealed new functions for ZBTB17 in the heart, a transcription factor that may play a role as a novel cardiomyopathy gene.
Who Cares About Auditor Reputation?

I provide evidence on the demand for auditor reputation by examining the defections of Arthur Andersen LLP's clients following the accounting scandals and criminal conviction marring the auditor's reputation in 2002. About 95 percent of clients in my sample did not switch auditors until after Andersen was indicted for criminal misconduct regarding its failed audit of Enron Corp. I test whether the timing of client defections and the choice of a new auditor are consistent with managers' incentives to mitigate potentially costly information and agency problems. I find that clients defected sooner, mostly to another Big 5 auditor, if they were more visible in the capital markets; such clients attracted more analysts and press coverage, had larger institutional ownership and share turnover, and raised more cash in recent security issues. However, my proxies for agency conflicts - managerial ownership and financial leverage - are not associated with the timing of defections or the choice of new auditor. Overall, my study suggests that firms more visible in the capital markets tend to be more concerned about engaging highly reputable auditors, consistent with such firms trying to build and preserve their own reputations for credible financial reporting.
Program Code Generator for Cardiac Electrophysiology Simulation with Automatic PDE Boundary Condition Handling

Clinical and experimental studies involving human hearts can have certain limitations. Methods such as computer simulations can be an important alternative or supplemental tool. Physiological simulation at the tissue or organ level typically involves the handling of partial differential equations (PDEs). Boundary conditions and distributed parameters, such as those used in pharmacokinetics simulation, add to the complexity of the PDE solution. These factors can tailor PDE solutions and their corresponding program code to specific problems. Boundary condition and parameter changes in the customized code are usually prone to errors and time-consuming. We propose a general approach for handling PDEs and boundary conditions in computational models using a replacement scheme for discretization. This study is an extension of a program generator that we introduced in a previous publication. The program generator can generate code for multi-cell simulations of cardiac electrophysiology. Improvements to the system allow it to handle simultaneous equations in the biological function model as well as implicit PDE numerical schemes. The replacement scheme involves substituting all partial differential terms with numerical solution equations. Once the model and boundary equations are discretized with the numerical solution scheme, instances of the equations are generated to undergo dependency analysis. The result of the dependency analysis is then used to generate the program code. The resulting program code are in Java or C programming language. To validate the automatic handling of boundary conditions in the program code generator, we generated simulation code using the FHN, Luo-Rudy 1, and Hund-Rudy cell models and run cell-to-cell coupling and action potential propagation simulations. One of the simulations is based on a published experiment and simulation results are compared with the experimental data. We conclude that the proposed program code generator can be used to generate code for physiological simulations and provides a tool for studying cardiac electrophysiology.
High-speed cam design for quick-action linkage system

As a specially designed mechanism, cam is usually be used in many high-speed structures and mechanisms. Diverse geometric cam counters support for diverse motion applications, including the displacement, the velocity and the acceleration. This paper proposes a design of a high-speed cam which can be applied in a quick-action-based four bar linkage system. This variable high-speed cam is design by using the graphical combination of 5th and 7th polynomial cam curves. In addition, the kinematic model of a linkage system based on our proposed cam is constructed. Finally, the simulation results of the cam counter, velocity and acceleration of the designed cam are shown to validate the appropriate application of a quick-action linkage system.
A simple rate distortion estimation for embedded block coding

The embedded block coding with optimized truncation (EBCOT) algorithm has been adopted by the JPEG2000 standard. In which, the post compression rate distortion (PCRD) optimization needs a large memory space to store all the code streams of code blocks; however, code blocks of less importance might not be used for the optimal decoded image at a bit rate. To avoid waste of computational power and memory space, a simple context based rate distortion estimation (CBRDE) is proposed to arrange the scanning order of code blocks in an adaptive manner. CBRDE is based on the MQ table, which is available at both encoder and decode. Thus, there is no need to store and transmit the rate distortion information of code blocks. Experimental results show that the rate distortion curves are almost convex; this demonstrates the benefit of the proposed CBRDE.
High-z radio starbursts host X-ray AGN

Abstract We use Virtual Observatory methods to investigate the association between radio and X-ray emission at high redshifts. Fifty-five of the 92 HDF(N) sources resolved by combining MERLIN+VLA data were detected by Chandra, of which 18 are hard enough and bright enough to be obscured AGN. The high-z population of μJy radio sources is dominated by starbursts an order of magnitude more active and more extended than any found at z < 1 and at least a quarter of these simultaneously host highly X-ray-luminous obscured AGN.
Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data

The assessment of pavement cracks is one of the essential tasks for road maintenance. This paper presents a novel framework, called ITVCrack, for automated crack extraction based on iterative tensor voting (ITV), from high-density point clouds collected by a mobile laser scanning system. The proposed ITVCrack comprises the following: 1) the preprocessing involving the separation of road points from nonroad points using vehicle trajectory data; 2) the generation of the georeferenced feature (GRF) image from the road points; and 3) the ITV-based crack extraction from the noisy GRF image, followed by an accurate delineation of the curvilinear cracks. Qualitatively, the method is applicable for pavement cracks with low contrast, low signal-to-noise ratio, and bad continuity. Besides the application to GRF images, the proposed framework demonstrates much better crack extraction performance when quantitatively compared to existing methods on synthetic data and pavement images.
Radio Sources in Low-Luminosity Active Galactic Nuclei. II. Very Long Baseline Interferometry Detections of Compact Radio Cores and Jets in a Sample of LINERs

We have used the VLBA at 5 GHz to observe all galaxies with nuclear radio flux densities above 3.5 mJy found in a VLA survey at 15 GHz of a sample of nearby LINER galaxies. All galaxies were detected revealing high brightness temperature (Tb ≳ 108 K) radio sources. Free-free emission is unlikely since it greatly overpredicts the soft X-ray luminosities. We infer the presence of active galactic nucleus (AGN)-like, nonthermal radio emission most likely powered by underfed black holes. Together with our VLA sample we estimate from our observations that at least one-half of LINER galaxies host genuine AGNs. We find no evidence for highly inverted radio cores as predicted in the advection-dominated accretion flow model: the (nonsimultaneous) spectral indices are on average around α = 0.0. In the two brightest sources we detect some extended emission, which appears to originate in jets in at least one of these galaxies. Together with the spectral indices this suggests that the nuclear emission at centimeter radio waves is largely dominated by emission from radio jets, very similar to the situation in more luminous AGNs. The energy released in these jets could be a significant fraction of the energy budget in the accretion flow.
Optimization of the constant power speed range of a saturated permanent magnet synchronous motor

This paper proposes an optimization procedure for interior permanent magnet synchronous machines to achieve wide speed operation. This method applies on motors with finite maximum speed. The method takes into account the effect of saturation. Non-linear models of the d- and q-axis flux linkages are obtained from finite element analysis and are used to derive the power capability of the machine. These models are combined with response surface method and genetic algorithms to optimize its constant power speed range
Shaping pulses to control bistable biological systems

In this paper, we present a framework for shaping pulses to control biological systems, and specifically systems in synthetic biology. By shaping we mean computing the magnitude and the length of a pulse, application of which results in reaching the desired control objective. Hence the control signals have only two parameters, which makes these signals amenable to wetlab implementations. We focus on the problem of switching between steady states in a bistable system. We show how to estimate the set of the switching pulses, if the trajectories of the controlled system can be bounded from above and below by the trajectories of monotone systems. We then generalise this result to systems with parametric uncertainty under some mild assumptions on the set of admissible parameters, thus providing some robustness guarantees. We illustrate the results on some example genetic circuits.
Transient emotional events and individual affective traits affect emotion recognition in a perceptual decision-making task

Both affective states and personality traits shape how we perceive the social world and interpret emotions. The literature on affective priming has mostly focused on brief influences of emotional stimuli and emotional states on perceptual and cognitive processes. Yet this approach does not fully capture more dynamic processes at the root of emotional states, with such states lingering beyond the duration of the inducing external stimuli. Our goal was to put in perspective three different types of affective states (induced affective states, more sustained mood states and affective traits such as depression and anxiety) and investigate how they may interact and influence emotion perception. Here, we hypothesized that absorption into positive and negative emotional episodes generate sustained affective states that outlast the episode period and bias the interpretation of facial expressions in a perceptual decision-making task. We also investigated how such effects are influenced by more sustained mood states and by individual affect traits (depression and anxiety) and whether they interact. Transient emotional states were induced using movie-clips, after which participants performed a forced-choice emotion classification task with morphed facial expressions ranging from fear to happiness. Using a psychometric approach, we show that negative (vs. neutral) clips increased participants’ propensity to classify ambiguous faces as fearful during several minutes. In contrast, positive movies biased classification toward happiness only for those clips perceived as most absorbing. Negative mood, anxiety and depression had a stronger effect than transient states and increased the propensity to classify ambiguous faces as fearful. These results provide the first evidence that absorption and different temporal dimensions of emotions have a significant effect on how we perceive facial expressions.
Exploiting Item Representations for Soft Clustering Recommendation

Recommender systems help dealing with the information overload problem since they provide personalized content for users. There are two major paradigms in recommendation: content-based and collaborative filtering. Regardless of the paradigm, there has been a great effort into finding additional information to better describe items and/or users, which in turn helps to increase the personalization power of the system. User's reviews turn out to be a great source of information, since they provide information about the characteristics of the items as well as insights about the opinion of the user towards them. In previous works, we explored some techniques for extracting information from reviews in order to generate items' representations and applied them into an item k-NN algorithm. In this work, we explore the impact that those representations, alongside with rating and genre-based representations, can cause into a soft clustering-based recommender system. We compare our findings with the item k-NN algorithm and observe that they are better in some cases, but the soft clustering recommender has lower computational cost.
$H^1$ Solutions of a class of fourth order nonlinear equations for image processing

Recently fourth order equations of the form $u_t = -\nabla\cdot((\mathcal G(J_\sigma u)) \nabla \Delta u)$ have been proposed for noise reduction and simplification of two dimensional images. The operator $\mathcal G$ is a nonlinear functional involving the gradient or Hessian of its argument, with decay in the far field. The operator $J_\sigma$ is a standard mollifier. Using ODE methods on Sobolev spaces, we prove existence and uniqueness of solutions of this problem for $H^1$ initial data.
Proof of Correctness for ASOCS AA 3 Networks 1

This paper analyzes Adaptive Algorithm 3 (AA3) of Adaptive Self-Organizing Concurrent Systems (ASOCS) and proves that AA3 correctly fulfills the rules presented. Several different models for ASOCS have been developed. AA3 uses a distributed mechanism for implementing rules so correctness is not obvious. An ASOCS is an adaptive network composed of many simple computing elements operating in parallel. An ASOCS operates in one of two modes: learning and processing. In learning mode, rules are presented to the ASOCS and incorporated in a self-organizing fashion. In processing mode, the ASOCS acts as a parallel hardware circuit that performs the function defined by the learned rules.
SIMMUNE, a tool for simulating and analyzing immune system behavior

We present a new approach to the simulation and analysis of immune system behavior. The simulations that can be done with our software package called SIMMUNE are based on immunological data that describe the behavior of immune system agents (cells, molecules) on a microscopial (i.e. agent-agent interaction) scale by defining cellular stimulus-response mechanisms. Since the behavior of the agents in SIMMUNE can be very flexibly configured, its application is not limited to immune system simulations. We outline the principles of SIMMUNE's multiscale analysis of emergent structure within the simulated immune system that allow the identification of immunological contexts using minimal a priori assumptions about the higher level organization of the immune system.
Distributed metadata management scheme in cloud computing

Metadata management is critical to distributed file system. Many large-scale storage systems such as HDFS involve the decoupling of metadata management from file data access. In such architecture, a single master server manages all metadata, while a number of data servers store file data. This architecture can't meet the exponentially increased storage demand in cloud computing, as the single master server may become a performance bottleneck. This paper presents a metadata management scheme based on HDFS for cloud computing. Our scheme employs multiple Namenodes, and divides the metadata into “buckets” which can be dynamically migrated among Namenodes according to system workloads. To maintain reliability, metadata is replicated in different Namenodes with log replication technology, and Paxos algorithm is adopted to keep replication consistency. Besides, caching and prefetching technology is integrated to improve system performance.
A new ultralight anthropomorphic hand

In this paper a very lightweight artificial hand is presented that approximates the manipulation abilities of a human hand very well. A large variety of different objects can be grasped reliably and the movements of the hand appear to be very natural. This five finger hand has 13 independent degrees of freedom driven by a new type of powerful small size flexible fluidic actuator. The actuators are completely integrated in the fingers which made possible the design of a very compact and lightweight hand that can either be used as a prosthetic hand or as a humanoid robot hand. A mathematical model for the expansion of a flexible fluidic actuator is given and the mechanical construction and features of the new anthropomorphic hand are illustrated.
The Application of Improved Elman Neural Network in the Exchange Rate Time Series

In this paper, we select the Elman neural network method to improve because of its good non-linear effect of disturbance elimination, and present a new exchange rate time series prediction method. We point out a new improved Elman neural network model firstly, and then predict the time series of RMB exchange rate against U. S. dollar. Through the forecasting process, we determine the input variables for the network structure, and determine the neural network’s critical parameters to forecasting. The results show that the improved Elman network can obtain better results during the forecasting process.