Miracle-dz/UniFAR-FTUs huggingface dataset:
Preference for leaders with high and low facial width-to-height ratios: moderating roles of political ideology and voting context

BACKGROUND It has been argued that human ancestors evolved greater sensitivity to certain traits that signal dominance in potential leaders. From this perspective, modern voters still favor certain physical characteristics during political elections. Indeed, previous studies have shown that voters prefer dominant candidates, especially when primed with wartime scenarios, and with conservative voters being more likely to choose a dominant leader. Because facial width-to-height ratio (fWHR) was found to be positively associated with perceived dominance, we sought to investigate the effect of fWHR on leader preference by taking into an account voting context and voters’ political ideology. PARTICIPANTS AND PROCEDURE A total of 148 participants took part in two online experiments in which we manipulated standardized facial images to represent faces with low and high fWHR. Furthermore, we assessed participants’ political ideology and asked them to rate the extent to which faces with low and high fWHR looked like leaders during wartime and peacetime scenarios. RESULTS Preference for leaders with high fWHR was positively related to participants’ political ideology, but only in a wartime scenario, suggesting that the more conservative participants were, the higher was their preference for leaders with high fWHR. This is consistent with the notion that preferences for dominant-looking leaders vary as a function of the contextual (voting context) and individual differences (political ideology). CONCLUSIONS The present findings provide new evidence for the contribution of fWHR in leader preference and significantly adds to the results of previous research demonstrating the roles of voters’ political ideology and politicians’ physical characteristics in perceiving leadership abilities.
Analysis of the Requirements of Settlement-Free Interconnection Policies

Peering between two networks may be either settlement-free or paid. In order to qualify for settlement-free peering, large Internet Service Providers (ISPs) require that peers meet certain requirements. However, the academic literature has not yet shown the relationship between these settlement-free peering requirements and the value to each interconnecting network. We develop two models to analyze the value to each network from the most common and important requirements in the United States. Large ISPs in the U.S. often require potential settlement-free peers to interconnect at a minimum of 6–8 locations. We find that there is a substantial benefit from this requirement to the ISP, but little incremental benefit from a larger number of interconnection points. Large ISPs often require that the ratio of incoming traffic to outgoing traffic remain below approximately 2:1. In the case of two interconnecting ISPs, we find that this requirement ensures a roughly equal exchange of value. We also show that it is rational for an ISP to agree to settlement-free peering if the content provider agrees to interconnect at a specified minimum number of interconnection points and to deliver a specified minimum proportion of traffic locally, but a limit on the traffic ratio is irrational.
Moderate Deviations of Dependent Random Variables Related to CLT

This paper consists of three parts. In the first part, we find a common condition-the C 2 -regularity-both for CLT and for moderate deviations. We show that this condition is verified in two important situations: the Lee-Yang theorem case and the FKG system case. In the second part, we apply the previous results to the additive functionals of a Markov process. By means of Feynman-Kac formula and Kato's analytic perturbation theory, we show that the Lee-Yang theorem holds under the assumption that 1 is an isolated, simple and the only eigenvalue with modulus 1 of the operator P 1 acting on an appropriate Banach space (bE, C b (E), L 2 . ). The last part is devoted to some applications to statistical mechanical systems, where the C 2 -regularity becomes a property of the pressure functionals and the two situations presented above become exactly the Lee-Yang theorem case and the FKG system case. We shall discuss in detail the ferromagnetic model and give some general remarks on some other models.
Association between rest-activity rhythm and cognitive function in the elderly: The U.S. National Health and Nutrition Examination Survey, 2011-2014

Background Circadian rhythm plays an essential role in various physiological and pathological processes related to cognitive function. The rest-activity rhythm (RAR) is one of the most prominent outputs of the circadian system. However, little is known about the relationships between RAR and different domains of cognitive function in older adults. The purpose of this study was to examine the relationships between RAR and various fields of cognitive function in older Americans. Methods This study included a total of 2090 older adults ≥ 60 years old from the National Health and Nutrition Examination Survey (NHANES) in 2011-2014. RAR parameters were derived from accelerometer recordings. Cognitive function was assessed using the word learning subtest developed by the Consortium to Establish a Registry for Alzheimer’s disease (CERAD W-L), the Animal Fluency Test (AFT) and the Digital Symbol Substitution Test (DSST). Linear regression was used to determine the relationships between RAR parameters (IS, IV, RA, L5, M10) and cognitive function scores (CERAD W-L, AFT, DSST). Results After adjusting for potential confounders, lower IS and M10 were associated with lower CERAD W-L scores (P=0.033 and P=0.002, respectively). Weaker RA and higher L5 were associated with lower AFT scores (P<0.001 and P=0.001, respectively). And lower IS, RA, and higher L5 were associated with lower DSST scores (P=0.019, P<0.001 and P<0.001, respectively). In addition, the results of sensitivity analysis were similar to those of our main analyses. The main correlation results between the RAR indicators and cognitive function were robust. Conclusions This study suggested that the weakened and/or disrupted RAR was associated with cognitive decline in different domains in Americans over the age of 60.
Exploring the effects of ventilation and air-conditioned environments, on droplet and airborne transmission of SARS-COV-2

To assess the implications of air conditioning and ventilation on droplet and airborne transmission of SARS-COV-2, several scientific research databases were searched and cross-referenced. Then, an analysis was conducted on the findings pertinent to interaction between several environmental variables affected by HVAC systems and their effect on Virus transmission. The results suggest that airflow velocity may interfere with the trajectories of large respiratory droplets and aerosols. Lower relative humidity provided suitable conditions for virus survival whereas higher temperatures increased aerosol formation, but were detrimental to virus survival. Suboptimal temperatures and humidity can compromise pathogen filtration functions in the nose, while proper use of HVAC functions can help preserve them. Transmission of SARS-COV-2 is not affected solely by the virus’s internal properties. Ambient conditions, whether natural or modified by HVAC systems can have a significant effect on the transmissibility and virulence of both the virus and virus-related sickness. The current infection prevention measures, such as social distancing, need to be revised in certain scenarios where natural ventilation or HVAC systems are involved. This will offer, hopefully, higher protection from infections with SARS-COV-2 and similar pathogens.
Application of high‐order quadrature rules to time‐domain boundary element analysis of viscoelasticity

This paper presents an accurate and efficient method for the treatment of time integrals in time-domain boundary integral equations for viscoelastic problems. First, the integral equations in space and time are derived for linear viscoelastic and thermo-viscoelastic problems. Then various quadrature rules are discussed for the computation of time integrals. A modified Simpson's rule is proposed as an effective integration scheme. The proposed procedure requires a small computational effort and it is applicable even in time-domain boundary element analysis of many different problems. Numerical results of example problems show the accuracy and effectiveness of the proposed method.
Weak Seismic Signal Enhancement Using Curvelet Transform and Compressive Sampling

Conventional curvelet-domain denoising methods suppress random noise by thresholding the amplitude of curvelet coefficients, which makes it hard to distinguish weak seismic signals from random noise because they share the same characteristic of weak amplitude in the curvelet domain. Here, we put forward an innovative weak seismic signal enhancement method that can distinguish weak seismic signals from random noise. After compressive sampling (CS), the curvelet coefficients of weak seismic signals show significant amplitude reduction, whereas random noise does not. We take advantage of this characteristic and design a sensitivity coefficient, the absolute ratio of curvelet coefficients before and after CS. The sensitivity coefficient can distinguish weak seismic signals from random noise in the curvelet domain better than thresholding the amplitude of curvelet coefficients. The results of synthetic and field seismic data applications both indicate that our method outperforms the conventional curvelet-domain denoising method on weak seismic signal enhancement.
Multi-Bernoulli filtering for initially unresolved targets in clutter

Multiple target tracking (MTT) is a challenging task that aims to estimate the number of targets and their states in the presence of process noise, measurement noise and data association uncertainty. This paper considers a special MTT problem characterized by additional complexity. In this problem, multiple targets are launched simultaneously in nearby locations at the same speed with slightly diﬀerent directions. As the distances be-tween the initial locations of these targets are smaller than the resolution of the sensor, this results in merged measurements, i.e., unresolved tracks at the very beginning. To deal with this problem, the recently proposed Multi-Bernoulli (MB) ﬁlter is applied. Using a model for the merged measurements, simulation results with 2-D Cartesian measurements in an optical sensor’s focal plane in the presence of clutter show that the initially unresolved tracks become resolved with MB ﬁltering a few time steps after the measurements become resolved. Thus, the MB ﬁlter is capable of keeping track of the number of targets and their corresponding states when they are initially unresolved.
Oxidative stress on vessels at the maternal-fetal interface for female reproductive system disorders: Update

Considerable evidence shows that oxidative stress exists in the pathophysiological process of female reproductive system diseases. At present, there have been many studies on oxidative stress of placenta during pregnancy, especially for preeclampsia. However, studies that directly focus on the effects of oxidative stress on blood vessels at the maternal-fetal interface and their associated possible outcomes are still incomplete and ambiguous. To provide an option for early clinical prediction and therapeutic application of oxidative stress in female reproductive system diseases, this paper briefly describes the composition of the maternal-fetal interface and the molecular mediators produced by oxidative stress, focuses on the sources of oxidative stress and the signaling pathways of oxidative stress at the maternal-fetal interface, expounds the adverse consequences of oxidative stress on blood vessels, and deeply discusses the relationship between oxidative stress and some pregnancy complications and other female reproductive system diseases.
The Effect of Biological Corrosion on the Hydration Processes of Synthetic Tricalcium Aluminate (C3A)

This paper presents a study related to the biological degradation of a tricalcium aluminate (C3A) phase treated with reactive media from the agricultural industry. During one month of setting and hardening, synthetic C3A was subjected to corrosion in corn silage, pig slurry and chicken manure. The hardening process of the C3A phase in water was used as a reference sample. The phase composition and microstructure of the hydrating tricalcium aluminate slurries were characterised by X-ray diffraction (XRD), thermal analysis (DTA/TG/DTG/EGA), scanning microscopy (SEM, EDS) and infrared spectroscopy (FT-IR). In the samples studied, it was observed that the qualitative and quantitative phase composition of the synthetic tricalcium aluminate preparations changed depending on the corrosion exposure conditions. The main crystalline phases formed by the hydration of the examined samples in water as well as in corrosive media were the catoite (Ca3Al2(OH)12) and hydrocalumite (Ca2Al(OH)7·3H2O) phases. Detailed analysis showed the occurrence of secondary crystallisation in hydrating samples and the phases were mainly calcium carbonates (CaCO3) with different crystallite sizes. In the phase composition of the C3A pastes, varying amounts of aluminium hydroxides (Al(OH)3) were also present. The crystalline phases formed as a result of secondary crystallisation represented biological corrosion products, probably resulting from the reaction of hydrates with secondary products resulting from the metabolic processes of anaerobic bacterial respiration (from living matter) associated with the presence of bacteria in the reaction medium. The results obtained contribute towards the development of fast-acting and bio-corrosion-resistant special cements for use in bioenergetics.
Analysis of adhesively bonded joints: a finite element method and a material model with damage

This paper deals with the derivation of a finite element (FE) method for an adhesively bonded joint which consists of two relatively thin bodies, joined by an even thinner adhesive layer. It is based on a model of the compound joint where the three bodies involved are described as material surfaces. A geometrically two‐dimensional model, where the middle surfaces of the upper and lower body are represented as geometrically coinciding surfaces, is obtained.
Collagen Functionalization of Polymeric Electrospun Scaffolds to Improve Integration into Full-Thickness Wounds

Background: Electrospun fibers are widely studied in regenerative medicine for their ability to mimic the extracellular matrix (ECM) and provide mechanical support. In vitro studies indicated that cell adhesion and migration is superior on smooth poly(L-lactic acid) (PLLA) electrospun scaffolds and porous scaffolds once biofunctionalized with collagen. Methods: The in vivo performance of PLLA scaffolds with modified topology and collagen biofunctionalization in full-thickness mouse wounds was assessed by cellular infiltration, wound closure and re-epithelialization and ECM deposition. Results: Early indications suggested unmodified, smooth PLLA scaffolds perform poorly, with limited cellular infiltration and matrix deposition around the scaffold, the largest wound area, a significantly larger panniculus gape, and lowest re-epithelialization; however, by day 14, no significant differences were observed. Collagen biofunctionalization may improve healing, as collagen-functionalized smooth scaffolds were smallest overall, and collagen-functionalized porous scaffolds were smaller than non-functionalized porous scaffolds; the highest re-epithelialization was observed in wounds treated with collagen-functionalized scaffolds. Conclusion: Our results suggest that limited incorporation of smooth PLLA scaffolds into the healing wound occurs, and that altering surface topology, particularly by utilizing collagen biofunctionalization, may improve healing. The differing performance of the unmodified scaffolds in the in vitro versus in vivo studies demonstrates the importance of preclinical testing.
An Efficient Lightweight Generative Adversarial Network for Compressed Sensing Magnetic Resonance Imaging Reconstruction

Compressed-sensing-based magnetic resonance imaging (CS-MRI) methods can significantly shorten scanning time while ensuring reconstructed image quality. Recently, deep learning methods, particularly generative adversarial networks (GAN), have been introduced into CS-MRI. However, these GAN-based methods suffer from their heavy learning parameters and ignore long-range dependency, which degrades the reconstructed image quality. Thus, the objective of this study is to design an efficient lightweight GAN to achieve more accurate MRI reconstruction. The proposed framework, named SepGAN, utilises depthwise separable convolution as the basic component to reduce the number of learning parameters. Two modules, the dilated depthwise separable convolution dense block and a squeeze-and-excitation lightweight self-attention module were proposed to extract the long-range dependency and improve the representational ability. The focal frequency loss was also involved in assisting the model to focus on high-frequency information. To evaluate the performance of the three proposed methods and SepGAN, two brain datasets were used in our experiment. From the results of our comparison analysis, SepGAN possesses the minimum number of parameters and multiply–accumulate operations (i.e., 7.32 M and 13.62G) and outperforms other methods in a variety of evaluation metrics, especially in Frechet inception distance, proving that reconstructed images of our method have better visual effects. For unseen pathological data, SepGAN can also perform effective reconstruction with explicit tumour textures and boundaries. The experimental results demonstrate that SepGAN can reconstruct high quality images with fewer parameters and exhibit remarkable generalisation ability.
Pearson-readhead survey sources: properties of the centimeter-wavelength flux and polarization of a complete radio sample

We have carried out a monitoring program to observe systematically the integrated total flux density and linear polarization of sources in the Pearson-Readhead sample of extragalactic objects at 4.8, 8.0, and 14.5 GHz. The large data base obtained has been used to investigate the statistical behavior of the flux and polarization in this well-defined sample and to search for dependence of these properties on other radio and optical properties
Algebraic properties of semi-simple lattices and related groups.

Two abstract theories are developed. The first concerns isomorphism in variants with the same multiplicative properties as the Euler characteristic. It is used to show that the index of a subgroup in a semi-simple lattice is deter mined by its isomorphism type when that index is finite. This is also proved to be the case for subgroups of finite index in free products of finitely many semi-simple lattices as well as certain non-trivial extensions of Z by surface groups. In addition, a criterion for the failure of this property is given which applies to a large class of central extensions. The second development concerns the syzygies of groups. The results of this theory are used to define the cohomology groups of a duality group in terms of morphisms between stable modules in the derived category. The Farrell cohomology of virtual duality groups is also considered.
A Longitudinal Study with a Laser Methane Detector (LMD) Highlighting Lactation Cycle-Related Differences in Methane Emissions from Dairy Cows

Simple Summary Within anthropogenic sources, agriculture contributes the most to greenhouse gas (GHG) emissions. The on-field assessment of methane emissions from livestock is crucial for testing and implementing mitigation strategies to reduce the deleterious effects of GHG on climate. Therefore, this article presents a longitudinal study in which measurements of enteric methane emissions from dairy cows (Jersey and Holstein-Freisian raised under a semi-extensive management system) were performed with a tool previously described as reliable, portable, and cost-effective: the laser methane detector (LMD). The results showed breed-related differences in methane emissions when milk yield was not considered, challenging us to rethink milk traits and breeding choices. Moreover, methane emissions were lower in cows in early lactation as well as in primiparous, likely reflecting concentrate supplementation and energy metabolism. The study pointed out the opportunity to design and test regional tailored mitigation strategies, including nutritional management, for higher methane emission periods within cows’ lactation cycle, while ensuring the enhancement of productivity. Abstract Reversing climate change requires broad, cohesive, and strategic plans for the mitigation of greenhouse gas emissions from animal farming. The implementation and evaluation of such plans demand accurate and accessible methods for monitoring on-field CH4 concentration in eructating breath. Therefore, this paper describes a longitudinal study over six months, aiming to test a protocol using a laser methane detector (LMD) to monitor CH4 emissions in semi-extensive dairy farm systems. Over 10 time points, CH4 measurements were performed in dry (late gestation) and lactating cows at an Azorean dairy farm. Methane traits including CH4 concentration related to eructation (E_CH4) and respiration (R_CH4), and eructation events, were automatically computed from CH4 measured values using algorithms created for peak detection and analysis. Daily CH4 emission was estimated from each profile’s mean CH4 concentration (MEAN_CH4). Data were analyzed using a linear mixed model, including breed, lactation stage, and parity as fixed effects, and cow (subject) and time point as random effects. The results showed that Holsteins had higher E_CH4 than Jersey cows (p < 0.001). Although a breed-related trend was found in daily CH4 emission (p = 0.060), it was not significant when normalized to daily milk yield (p > 0.05). Methane emissions were lower in dry than in lactation cows (p < 0.05) and increased with the advancement of the lactation, even when normalizing it to daily milk yield (p < 0.05). Primiparous cows had lower daily CH4 emissions related to R_ CH4 compared to multiparous (p < 0.001). This allowed the identification of periods of higher CH4 emissions within the milk production cycle of dairy cows, and thus, the opportunity to tailor mitigation strategies accordingly.
Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling

This paper develops a forecasting-based missing value replacement model for Phasor Measurement Unit (PMU) data during power system events. The proposed forecasting model leverages a sequence-to-sequence (Seq2Seq) long short-term memory (LSTM) network with an attention mechanism, which is trained with both pre-event and post-event data. The trained forecasting model is utilized to accurately estimate and recover missing measurements in PMU data. To improve the accuracy of the proposed model, we introduced two novel techniques: 1) integrating a prior knowledge matrix into the attention mechanism that effectively preserves correlations within PMU data, and 2) decoupling the magnitude and trend components of the residual forecast so that such forecasting models are separately trained, which boosts the resistance to the noisy signal. Numerical studies on real-world PMU data collected from the North American electric power grid demonstrate that our proposed model achieves 5% to 30% error reduction compare to the baseline models for all key power grid measurements in both root mean square error and mean absolute error metrics. Furthermore, our model exhibits robust missing data recovery performance even when nearly all of the grid event data is missing.