Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender Ideology and Women's Labor Market Transitions Within Couples in the Netherlands

Objective This study examines the role of women's and their partners' gender ideology in shaping women's labor market entries, exits, and changes in hours of employment. Background Recent research argues that women's gender ideology is crucial for understanding women's contemporary labor market participation. However, the role of male partners' gender ideology for partnered women's labor market participation has received less attention. Method The analysis uses three waves of a large‐scale household panel survey based on a random sample of individuals within Dutch households. Random‐effect models are applied to study whether women's and their partners' gender ideology are associated with women's labor market transitions and whether relevant household characteristics' associations with women's labor market transitions are conditional on both partners' gender ideology. Results Women's gender ideology is associated with the probability of women's labor market entries and exits, but not with changes in women's hours worked, whereas their male partners' ideology is related only to the probability of women's labor market exits. Furthermore, the negative association of having children with changes in women's hours worked is stronger for traditional compared to egalitarian women. There is no clear evidence that gender ideology moderates the association of the male partner's labor market resources with women's labor market transitions. Conclusion Women's labor market transitions are not only reactions to economic pressure and institutional constraints but also women's and marginally their partners' gender attitudes.
V3-Independent Competitive Resistance of a Dual-X4 HIV-1 to the CXCR4 Inhibitor AMD3100

A CXCR4 inhibitor-resistant HIV-1 was isolated from a dual-X4 HIV-1 in vitro. The resistant variant displayed competitive resistance to the CXCR4 inhibitor AMD3100, indicating that the resistant variant had a higher affinity for CXCR4 than that of the wild-type HIV-1. Amino acid sequence analyses revealed that the resistant variant harbored amino acid substitutions in the V2, C2, and C4 regions, but no remarkable changes in the V3 loop. Site-directed mutagenesis confirmed that the changes in the C2 and C4 regions were principally involved in the reduced sensitivity to AMD3100. Furthermore, the change in the C4 region was associated with increased sensitivity to soluble CD4, and profoundly enhanced the entry efficiency of the virus. Therefore, it is likely that the resistant variant acquired the higher affinity for CD4/CXCR4 by the changes in non-V3 regions. Taken together, a CXCR4 inhibitor-resistant HIV-1 can evolve using a non-V3 pathway.
A SUSTAINABLE APPROACH TO AUTOMATE USER SESSION BASED STATE MACHINE GENERATION FOR AJAX WEB APPLICATIONS

A whole new generation WEB 2.0 is being written to take advantage of extreme dynamism as in AJAX. AJAX is used to build rich internet applications that are more interactive, responsive, and easy to use. With the advent of Ajax which involves extreme dynamism, novel problems add to those already known in the Web testing area. For Testing an AJAX application, a sustainable approach is required to detect faults embedded area. Faults embedded areas are those areas where states of web application will get changed. Here, in this research paper, our focus will be on generating state machine of dynamic behaviour of an AJAX web application. To detect faults in any web application optimum approach is model based testing. Here intention is to generate effective state machine which will be able to detect all dynamic states of user session applied on web application. Therefore, we designed a framework to generate state machine of user sessions performed on web application. A prototype tool is developed to automate and validate the entire process. We applied whole process on five case studies and results have shown that we are able successfully detect Events, DOM changing Elements and States.
A new inexact iterative hard thresholding algorithm for compressed sensing

Compressed sensing (CS) demonstrates that a sparse, or compressible signal can be acquired using a low rate acquisition process below the Nyquist rate, which projects the signal onto a small set of vectors incoherent with the sparsity basis. In this paper, we propose a new framework for compressed sensing recovery problem using iterative approximation method via L0 minimization. Instead of directly solving the unconstrained L0 norm optimization problem, we use the linearization and proximal points techniques to approximate the penalty function at each iteration. The proposed algorithm is very simple, efficient, and proved to be convergent. Numerical simulation demonstrates our conclusions and indicates that the algorithm can improve the reconstruction quality.
The Relationship between Maximal Urethral Closure Pressure and Functional Urethral Length in Anterior Vaginal Wall Prolapse Patients According to Stage and Age

MUCP (Maximal urethral closure pressure) is known to be increased in patients with vaginal wall prolapse due to the mechanical obstruction of the urethra. However, urethral function following reduction has not yet been completely elucidated. Predicting postoperative urethral function may provide patients with important, additional information prior to surgery. Thus, this study was performed to evaluate the relationship between MUCP and functional urethral length (FUL) according to stage and age in anterior vaginal wall prolapse patients. 139 patients diagnosed with anterior vaginal wall prolapse at Yonsei University Medical College (YUMC) from March 1999 to May 2003 who had underwent urethral pressure profilometry following reduction were included in this study. The stage of pelvic organ prolapse (POP) was determined according to the dependent portion of the anterior vaginal wall (Aa, Ba). (By International Continence Society's POP Quantification system) Patients were divided into one of four age groups: patients in their 40s (n=13), 50s (n=53), 60s (n=54), and 70 and over (n=16). No difference in MUCP was found between the age groups. The FUL of patients in their 40s was shorter than that of patient's in their 50s and 60s. Patients were also divided into stages: stage II (n=35), stage III (n=76), and stage IV (n=25). No significant difference in MUCP was found according to stage and FUL. However, a significant difference was noted between stage III and IV as stage IV was longer. Anterior vaginal wall prolapse is known to affect urethral function due to prolapse itself, but according to our study, prolapse itself did not alter urethral function. This suggests that, regardless of age and stage, prolapse corrective surgery does not affect the urethral function.
A qPCR Assay to Detect and Quantify Shiga Toxin-Producing E. coli (STEC) in Cattle and on Farms: A Potential Predictive Tool for STEC Culture-Positive Farms

Shiga toxin-producing E. coli (STEC), of various serogroups harboring the intimin gene, form a serious threat to human health. They are asymptomatically carried by cattle. In this study, a quantitative real-time PCR (qPCR) method was developed as a molecular method to detect and quantify Shiga toxin genes stx1 and stx2 and the intimin gene eae. Subsequently, 59 fecal samples from six farms were tested using qPCR and a culture method as a reference. Three farms had contaminated animals as demonstrated by the culture method. Culture-positive farms showed moderate significantly higher stx prevalences than culture-negative farms (p = 0.05). This is the first study which showed preliminary results that qPCR can predict STEC farm contamination, with a specificity of 77% and a sensitivity of 83%, as compared with the culture method. Furthermore, the presence or quantity of stx genes in feces was not correlated to the isolation of STEC from the individual animal. Quantitative data thus did not add value to the results. Finally, the detection of both stx and eae genes within the same fecal sample or farm using qPCR was not correlated with the isolation of an eae-harboring STEC strain from the respective sample or farm using the culture method.
Antileishmanial effect of silver nanoparticles and their enhanced antiparasitic activity under ultraviolet light

Leishmaniasis is a protozoan vector-borne disease and is one of the biggest health problems of the world. Antileishmanial drugs have disadvantages such as toxicity and the recent development of resistance. One of the best-known mechanisms of the antibacterial effects of silver nanoparticles (Ag-NPs) is the production of reactive oxygen species to which Leishmania parasites are very sensitive. So far no information about the effects of Ag-NPs on Leishmania tropica parasites, the causative agent of leishmaniasis, exists in the literature. The aim of this study was to investigate the effects of Ag-NPs on biological parameters of L. tropica such as morphology, metabolic activity, proliferation, infectivity, and survival in host cells, in vitro. Consequently, parasite morphology and infectivity were impaired in comparison with the control. Also, enhanced effects of Ag-NPs were demonstrated on the morphology and infectivity of parasites under ultraviolet (UV) light. Ag-NPs demonstrated significant antileishmanial effects by inhibiting the proliferation and metabolic activity of promastigotes by 1.5- to threefold, respectively, in the dark, and 2- to 6.5-fold, respectively, under UV light. Of note, Ag-NPs inhibited the survival of amastigotes in host cells, and this effect was more significant in the presence of UV light. Thus, for the first time the antileishmanial effects of Ag-NPs on L. tropica parasites were demonstrated along with the enhanced antimicrobial activity of Ag-NPs under UV light. Determination of the antileishmanial effects of Ag-NPs is very important for the further development of new compounds containing nanoparticles in leishmaniasis treatment.
A modular robotic infrastructure to support planetary surface operations

This paper presents a study of a Planetary Surface Modular Robotic System (PSMRS) to support surface exploration of the Moon and Mars. For these missions a wide variety of tasks, requiring large a variation in robot capabilities, will need to be performed. Also, not all tasks can be foreseen. An approach is proposed that consists of a group of modules that are assembled to produce a robot for a specific task. It promotes efficiency and reliability through adaptability. This study investigated the concept's feasibility. Mission requirements were studied, important tasks were identified, and an inventory of modules was designed. Nine robots are described as examples of the diversity of robots (and capabilities) that can be produced. All nine robots were constructed using only 26 modules, showing the benefits in terms of launch mass and volume. Three robots were simulated performing representative, mission-relevant tasks including soil manipulation, instrument deployment, and science sample collection. The simulations demonstrate the approach's scientific feasibility and credibility.
The changing political impact of compassion-evoking pictures: the case of the drowned toddler Alan Kurdi

ABSTRACT Compassion-evoking and widely circulated news images seem capable of affecting public opinion and change history. A case in point is the picture of the drowned toddler Alan Kurdi, who died while trying to escape to Europe with his family in September 2015. We theorise how these types of photos affect public opinion. By relying on panel data with sequentially embedded survey experiments conducted in the aftermath of the picture’s publication and while large numbers of refugees were coming to Europe, we demonstrate that the image of Alan Kurdi had context-varying effects on policy preferences. Initially, the upsetting photo of the toddler increased support for liberal refugee policies across ideological divides. However, when individuals had the time to think about policy implications, they started processing even this highly upsetting picture through their ideology. Consequently, our results indicate that compassion-evoking images shift meaning over time.
An evaluation of the efficacy of methods used in screening for lower-limb arterial disease in diabetes.

OBJECTIVE Foot-related disease is the most common cause for hospital admission among the diabetic population. Lower-limb peripheral arterial occlusive disease (PAOD) is a major risk factor in diabetic foot disease. Screening for PAOD commonly includes foot pulses and the ankle-brachial pressure index (ABPI) and/or the toe-brachial pressure index (TBI), but concerns persist regarding their accuracy. We evaluated the efficacy of several commonly used screening methods in different subject populations. RESEARCH DESIGN AND METHODS We studied 130 limbs in 68 individuals with no critical ischemia over 8 months. Limbs were grouped on the basis of the presence or absence of diabetes, clinically detectable peripheral neuropathy, and PAOD identified on color duplex imaging. Comparative analyses of foot pulses, the ABPI, the TBI, and distal Doppler waveform analysis were performed. RESULTS Foot pulses, the TBI, and qualitative waveform analyses were highly sensitive screening methods in individuals with and without diabetes. However, detectable peripheral neuropathy was associated with a reduced sensitivity and poor specificity of foot pulses, a reduction in sensitivity of the ABPI (71 to 38%), and a reduction in specificity of the TBI (81 to 61%) and qualitative waveform analysis (96 to 66%). Quantitative analysis failed to detect disease with severely damped and low-intensity signals. CONCLUSIONS Screening tools that are effective in screening for lower-limb PAOD in the nondiabetic population are less efficacious in diabetes, particularly in the presence of detectable peripheral neuropathy. Qualitative waveform analysis and the TBI were demonstrated to be more effective screening methods than the ABPI and foot pulses particularly in high-risk limbs with detectable peripheral neuropathy.
Why a Comprehensive Understanding of Mental Workload through the Measurement of Neurovascular Coupling Is a Key Issue for Neuroergonomics?

Raja Parasuraman, the father of Neuroergonomics (the crossroads of Ergonomics and Neuroscience, Figure 1) has opened the doors to new discoveries and techniques for advancing understanding of human behavior with the underlying brain mechanisms (Parasuraman, 1998). As of his death in 2015, a precise and objective definition of the concept of mental workload (MWL) had still not yet been formulated. In this opinion piece, we posit that MWL is associated through the measurement of neurovascular coupling (NVC); innovative neuroimaging methods is now capable of measuring such a phenomenon; all while highlighting Parasuraman's many contributions to this field.
Debt-Prone Bugs: Technical Debt in Software Maintenance

Fixing bugs is an important phase in software development and maintenance. In practice, the process of bug fixing may conflict with the release schedule. Such confliction leads to a trade-off between software quality and release schedule, which is known as the technical debt metaphor. In this article, we propose the concept of debt-prone bugs to model the technical debt in software maintenance. We identify three types of debt-prone bugs, namely tag bugs, reopened bugs, and duplicate bugs. A case study on Mozilla is conducted to examine the impact of debt-prone bugs in software products. We investigate the correlation between debt-prone bugs and the product quality. For a product under development, we build prediction models based on historical products to predict the time cost of fixing bugs. The result shows that identifying debt-prone bugs can assist in monitoring and improving software quality.
Stochastic dependent-chance programming model and hybrid adaptive genetic algorithm for vendor selection problem

This paper proposes a stochastic dependent-chance programming model for vendor selection problem under the condition that the capacity, quality level, service level and lead time of each vendor are considered to be stochastic. Since stochastic programming is hard to solve by traditional methods, a hybrid adaptive genetic algorithm, which embeds the neutral network and stochastic simulation, is presented. To improve the performance of the algorithm, the probability of crossover and mutation will be adjusted according to the stage of evolution and fitness of the population. The solution procedure is tested on several randomly generated problems with varying parameters. The experimental results demonstrate that the hybrid adaptive genetic algorithm has strong adaptability.
Glucose turnover and metabolic and hormonal changes in ethanol-induced hypoglycaemia

Infusion of 67 g ethanol over four hours in fasted, non-obese normal men (a) induced hypoglycaemia by inhibiting gluconeogenesis; (b) produced noticeable increases in blood lactate, 3-hydroxybutyrate, and free fatty acid concentrations; (c) depressed plasma growth hormone concentrations, despite hypoglycaemia; and (d) raised plasma cortisol concentrations before significant hypoglycaemia occurred. These metabolic changes were explained by the reduction of redox state which accompanies ethanol oxidation. The pronounced changes in metabolic values recorded during this study suggested that the use of parenteral feeding regimens including ethanol needs to be reconsidered.
System for Heuristic and Rapid Processing of Component Layout and Wiring (SHARPCLAW)

A computer-aided design system for layout of printed wiring boards is described, with emphasis on component placement and conductor routing algorithms. The system is designed to operate with a data base; placement and routing may be accomplished automatically using force field techniques and a maze-following procedure, or manually with extensive error checking by the computer. Examples of the system output are given.
A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

learn-aBStract In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.
Cytomegalovirus and immunological aging: the real driver of HIV and heart disease?

HIV Study, Parrinello et al. [7] found that higher CMV immunoglobulin G (IgG) levels were not associated with markers of subclinical atherosclerosis among HIV-uninfected women, suggesting that increased antibody levels did not increase the risk of atherosclerosis in their HIV-uninfected subsample. In contrast, HIV-positive women with increased CMV IgG levels were more likely to have carotid artery stiffness. These same individuals did not show greater intima-media thickness nor presence of carotid lesions, indicating that the relationship between CMV IgG levels and markers of cardiovascular disease was specific for carotid artery stiffness in HIV-positive subjects. The relation between increased CMV IgG levels and carotid artery stiffness was consistentacrosstreated/aviremic,treated/ viremic, and untreated HIV-infected groups. However, when examining the association between CMV IgG levels and presence of carotid lesions by treatment/ viremia status groups, Parrinello and colleagues found that CMV IgG levels were associated with presence of carotid artery lesions only among HIV-infected persons who were receiving antiretroviral treatment and were aviremic, after adjustment for age, race, and smoking history. These results suggest that treatment/viremia status modifies the impact of CMV IgG level on carotid lesions.
Steganography of Data Embedding in Multimedia Images Using Interpolation and Histogram Shifting

In this paper, we propose a data hiding scheme with high capacity in steganography. First of all, we improve the data hiding technique based one neighbor mean interpolation for increasing the image quality of a stego-image without losing any capacity. A stego-image is derived from a cover image which has embedded the secret data. A cover image is generated from an original image by using interpolating method. Secondly, we apply histogram shifting technique to embed more secret data. The difference tables between the cover image and the stego-image except pivot pixels are calculated to make a histogram at first. Then, we modify the histogram data in an embedding procedure, and construct a new stego-image with two levels data hiding method. Our scheme can not only provide better image quality of the stego-image, but also have a very high capacity compared with other data hiding schemes based on interpolation.
HDR Recovery Under Rolling Shutter Distortions

Preserving the high dynamic irradiance of a scene is essential for many computer vision algorithms. In this paper, we develop a technique for high dynamic range (HDR) reconstruction from differently exposed frames captured with CMOS cameras which use a rolling shutter (RS) to good effect for reducing power consumption. However, because these sensors are exposed to the scene row-wise, any unintentional handshake poses a challenge for HDR reconstruction since each row experiences a different motion. We account for this motion in the irradiance domain by picking the correct warp for each row within a predefined search space. The RS effect is rectified and a clean frame is propagated from one exposure to another until we obtain rectified irradiance corresponding to all the exposures. The rectified irradiances are finally fused to yield an HDR map that is free from RS distortions.
Generalized Predictive Control With Actuator Deadband for Event-Based Approaches

This work presents an event-based control structure using the generalized predictive control (GPC) algorithm with actuator deadband. The main objective of this work is to limit the number of controlled system updates. In this approach, the controlled process is sampled with a constant sampling time and is updated in an asynchronous way that depends on the obtained control signal value. To achieve the desired control properties, a hybrid system framework is used to model the virtual actuator deadband. The deadband is modeled as system input constraints in the GPC's optimization procedure, exploring mixed integer quadratic programming (MIQP) techniques. The presented control structure considers adjustable actuator deadband as an additional tuning parameter and allows a tradeoff between control performance and the number of events/updates. This fact is very important for the lifetime of the actuators and minimizes their wear, especially for mechanical and electromechanical systems. The minimization of actuator usage can be also understood as an economical saving. Other benefits can be found in distributed control systems, where various control system agents are connected through computer networks, and where a reduction in communication is always welcome.