Miracle-dz/UniFAR-FTUs huggingface dataset:
Visually assessed body condition shows high heritability in a pedigreed great ape population

Body condition, a measure for relative fat mass, is associated with primate health, fitness, and overall welfare. Body condition is often influenced by dietary factors, age, and/or sex, but several body condition measures (body weight, weight‐to‐height ratios, and so on) also show high heritability across primate species, indicating a role of genetic effects. Although different measures for body condition exist, many require direct handling of animals, which is invasive, time‐consuming, and expensive, making them impractical in wild and captive settings. Therefore, noninvasive visual body condition score (BCS) systems were developed for various animal species, including macaques and chimpanzees, to visually assess relative fat mass. Here we evaluate the utility of a visual BCS system in bonobos by assessing (1) inter‐rater reliability, (2) links with body mass, a traditional hands‐on measure of condition, and (3) the factors driving individual variation in BCS. We adapted the chimpanzee BCS system to rate 76 bonobos in 11 European zoos (92% of the adult population). Inter‐rater reliability was high (s* = 0.948), BCSs were positively associated with body mass (β = 0.075) and not predicted by diet, sex, or age, nor were they associated with a higher abundance of obesity‐related diseases. Instead, BCSs showed high levels of heritability (h2 = 0.637), indicating that a majority of body condition variation in bonobos is attributable to genetic similarity of the individuals. This is in line with reported h2‐values for traditional body condition measures in primates and provides support for the reliability of visual BCS systems in great apes. The results of this study emphasize an often unanticipated role of genetics in determining primate body fat and health that has implications for the management of captive primates. Application of this tool in wild populations would aid to unravel environmental from genetic drivers of body condition variation in primates.
Roles for the Synechococcus elongatus RNA-Binding Protein Rbp2 in Regulating the Circadian Clock

The cyanobacterial circadian oscillator, consisting of KaiA, KaiB, and KaiC proteins, drives global rhythms of gene expression and compaction of the chromosome and regulates the timing of cell division and natural transformation. While the KaiABC posttranslational oscillator can be reconstituted in vitro, the Kai-based oscillator is subject to several layers of regulation in vivo. Specifically, the oscillator proteins undergo changes in their subcellular localization patterns, where KaiA and KaiC are diffuse throughout the cell during the day and localized as a focus at or near the pole of the cell at night. Here, we report that the CI domain of KaiC, when in a hexameric state, is sufficient to target KaiC to the pole. Moreover, increased ATPase activity of KaiC correlates with enhanced polar localization. We identified proteins associated with KaiC in either a localized or diffuse state. We found that loss of Rbp2, found to be associated with localized KaiC, results in decreased incidence of KaiC localization and long-period circadian phenotypes. Rbp2 is an RNA-binding protein, and it appears that RNA-binding activity of Rbp2 is required to execute clock functions. These findings uncover previously unrecognized roles for Rbp2 in regulating the circadian clock and suggest that the proper localization of KaiC is required for a fully functional clock in vivo.
From Infection to Death: An Overview of the Pathogenesis of Visceral Leishmaniasis

Kala-azar, also known as visceral leishmaniasis (VL), is a disease caused by Leishmania infantum and L. donovani. Patients experience symptoms such as fever, weight loss, paleness, and enlarged liver and spleen. The disease also affects immunosuppressed individuals and has an overall mortality rate of up to 10%. This overview explores the literature on the pathogenesis of preclinical and clinical stages, including studies in vitro and in animal models, as well as complications and death. Asymptomatic infection can result in long-lasting immunity. VL develops in a minority of infected individuals when parasites overcome host defenses and multiply in tissues such as the spleen, liver, and bone marrow. Hepatosplenomegaly occurs due to hyperplasia, resulting from parasite proliferation. A systemic inflammation mediated by cytokines develops, triggering acute phase reactants from the liver. These cytokines can reach the brain, causing fever, cachexia and vomiting. Similar to sepsis, disseminated intravascular coagulation (DIC) occurs due to tissue factor overexpression. Anemia, hypergammaglobulinemia, and edema result from the acute phase response. A regulatory response and lymphocyte depletion increase the risk of bacterial superinfections, which, combined with DIC, are thought to cause death. Our understanding of VL’s pathogenesis is limited, and further research is needed to elucidate the preclinical events and clinical manifestations in humans.
The influence of radio frequency‐based toothbrush on the accumulation of calculus and periodontal health: A randomized double‐blind controlled prospective study

The use of a toothbrush with radio frequency (RF) has shown to be of benefit regarding the reduction of plaque, calculus, and dental staining and improving teeth shade compared to conventional powered and manual toothbrushes. Aim: To evaluate the efficacy of the RF toothbrush in the reduction of calculus accumulation and its effect on periodontal parameters as well as subject satisfaction as compared to an identical sham‐tooth brush.
Employer Branding and COVID-19: A Job Demand–Resource Model for Employee Attraction and Retention in Health Sector

The healthcare sector includes a gamut of products and services, from manufacturing medical equipment/drugs and managing medical insurance to providing medical services. Professionals, including doctors, nurses, biomedical engineers, clinicians and other allied healthcare professionals, form the bulk of the human resources in the healthcare sector. During the COVID-19 pandemic, these workers were at the forefront of delivering preventive, palliative and curative health services. During this time, health sector employees faced enormous challenges at work, resulting in burnout and, thus, a higher rate of resignation. With burnout reported among healthcare professionals, the already overburdened health system had started crumbling and desperately needed human resources. Moreover, job seekers were reluctant to apply to the health sector during the COVID-19 pandemic. Thus, a dual challenge exists for healthcare organisations to attract and retain new employees. To overcome these challenges, companies in the healthcare industry need to brand themselves as one of the best employers, which could help attract and retain job seekers. Hence, this article proposes a theoretical model for attracting and retaining job seekers in the health sector based on the job demands–resources model. This article also suggests some propositions that could be later empirically tested.
A two-stage stochastic programming model for value-based supply chain network design

Nowadays, Value –based Supply Chain Management (VbSCM) is considered as a resource of competitive advantages, and companies with a long term strategic plan find the VbSCM as an effective factor in sustainability. In this context, supply chain network design has a significant impact on all value drivers (i.e. sales, supply chain costs, fixed assets and working capital). This paper proposes a stochastic mixed integer linear programming model for the value-based supply chain network design in which decisions on physical flow (raw materials and finished products) and financial flow are integrated. The proposed model is designed for a four-echelon, multi-commodity, multi-period supply chain and maximizes the value of the company based on the economic value-added concept by making some strategic and tactical decisions affecting the value drivers. Furthermore, a scenario-based two-stage stochastic programming model is developed with a scenario generation method based on Nataf transformation. Also, a computational analysis is done to illustrate performance of the proposed approach.
Mechanical properties and fracture behaviour of defective phosphorene nanotubes under uniaxial tension

The easy formation of vacancy defects and the asymmetry in the two sublayers of phosphorene nanotubes (PNTs) may result in brand new mechanical properties and failure behaviour. Herein, we investigate the mechanical properties and fracture behaviour of defective PNTs under uniaxial tension using molecular dynamics simulations. Our simulation results show that atomic vacancies cause local stress concentration and thus significantly reduce the fracture strength and fracture strain of PNTs. More specifically, a 1% defect concentration is able to reduce the fracture strength and fracture strain by as much as 50% and 66%, respectively. Interestingly, the reduction in the mechanical properties is found to depend on the defect location: a defect located in the outer sublayer has a stronger effect than one located in the inner layer, especially for PNTs with a small diameter. Temperature is also found to strongly influence the mechanical properties of both defect-free and defective PNTs. When the temperature is increased from 0 K to 400 K, the fracture strength and fracture strain of defective PNTs with a defect concentration of 1% are reduced further by 71% and 61%, respectively. These findings are of great importance for the structural design of PNTs as building blocks in nanodevices.
Material‐based process‐chain optimization in metal forming

The characteristics of a manufacturing product are influenced by a variety of different factors, such as the material properties of the base product. The prediction of properties that give optimal results in metal forming applications is a complex task but of high interest for the manufacturer. To realize such a prediction scheme, the process chain is split up into individual process steps and for each of them an inverse modeling is required. The specific aim of this work is to present an approach for the inverse problem formulation of a process step and to solve it using methods of machine learning. Moreover, the challenges that often arise due to the ill‐posed nature of inverse problems will be discussed. The main focus is on the crystallographic texture of metals, which strongly affects the deformation behavior during a process step and highly influences the characteristics of the final product. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Faint extended structures near galaxies: preliminary results from the Wise Observatory

Abstract We present the first results from a survey of deep imaging of edge-on galaxies, with the goal of testing the growth-by-accretion of galaxies proposed by ΛCDM. The data were obtained with a new telescope at the Wise Observatory. Our images show previously unreported extensions of the disk, tidal distortions, and streams at the level of 27-28 mag arcsec−2.
On the theory of the nonstationary excitation of a cavity

The theory of the excitation of cavities developed by L. A. Vainshtein is extended to the nonstationary case. Simulations of the excitation of cubic and plane-parallel cavities are presented. The results are generalized to the case of cavities with inhomogeneous impedance and magnetodielectric inserts (which can be linear or nonlinear).
Particle Finite Element Simulations of Cutting Processes in Manufacturing

The cutting of metals is an important process in manufacturing and challenges established methods in the field of computational mechanics. The particle finite element method (PFEM) combines the benefits of particle based methods and the standard finite element method (FEM) to account for large deformations and separation of material. In cutting simulations the workpiece is realised as a set of particles, whose boundary is detected by the α‐shape method. After the boundary detection, the particles are meshed with finite elements. Since metals show a plastic behavior under large deformations, a suitable material model needs to be considered. Numerical examples show the effect of the choice of the parameter α on the cutting force. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Predicting the Collapsibility Potential of Unsaturated Soils Using Adaptive Neural Fuzzy Inference System and Particle Swarm Optimization

Soil collapsibility is one of the important phenomena in unsaturated soil mechanics. This phenomenon can impose extensive financial damages on civil engineering structures due to soil subsidence. Because of uncertainties in effective parameters and their measurements, no precise mathematical relation has been proposed for collapsibility potential evaluation. Therefore, soft computing techniques such as fuzzy logic could be a suitable choice to account for different factors. Adaptive neural fuzzy inference system (ANFIS) was used in this study. To predict the collapsibility potential, hybrid algorithm and particles swarm optimization (PSO) were employed by ANFIS for system training. Gaussian membership functions were utilized for fuzzifying the data. Also, data classification was performed in a subtractive form in the fuzzy inference system. A total of 327 laboratory data was used in particles swarm algorithm, 266 of which were chosen for training and 66 for testing. The obtained results showed the effects of different parameters and the rate of their changes in collapsibility potential. Moreover, comparison of different approaches of system training was done using correlation coefficient. The superiority of the proposed method and the utilized techniques was shown by comparing the results with the ones obtained by other researches.
How stable are our standard candles? Time-resolved analysis of amplitude and period changes in OGLE-III LMC Cepheids

Abstract We revisit a group of OGLE-III Cepheids that exhibit nearby double peaks in their periodograms using a technique of statistics called kernel modelling. We investigate the phenomenology of these Cepheids (termed twin-peak Cepheids in this paper) in more detail by comparing a sample of 29 LMC Cepheids that exhibit twin frequency peaks with 24 other Cepheids that do not. Using the kernel technique, we investigate light curve variability as a function of time, revealing both frequency and amplitude modulations. We present the preliminary results of our study in progress, which suggests a complex interplay between the two types of modulations and their detectability via twin peaks in the periodogram. The study reveals the potential of the kernel technique to help theory and modelling with detailed data analyses, capable of tracing fine time-dependent variations of the phenomenology of pulsations for objects of surveys or observation campaigns producing sufficiently dense time sampling.
HOW_MANY_IMPUTATIONS: Stata module to determine required number of imputations

After mi estimate, run how_many_imputations to get the number of imputations needed to ensure that standard errors estimates would not change too much if the data were imputed again. After how_many_imputations, run mi estimate again with the recommended number of imputations. Or don't if the recommended number of imputations is less than you used the first time. This two-stage approach to choosing the number of imputations was developed by von Hippel (2018).
A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high-frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high-frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low-frequency component to retain the high-frequency component. Moreover, the most sensitive mode component is selected in these high-frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi-logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi-logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS-DFA) and EMD-DFA.
Contour detection using an improved holistically-nested edge detection network

Recently we have been concerned with locating and tracking targets in aerial videos. Targets in aerial videos usually have weak boundaries due to moving cameras. For the purpose of target detecting, detecting the contour of the target is needed and can help with improving the accuracy of target tracking. Edge detection has assisted in obtaining some advances in this effort. However, noisy images and weak boundary limit the performance of existing contour detecting algorithms. After analyzing the structures and edge maps of a Holistically-nested Edge Detection network, we utilize the highest level side-output and improve the architecture of HED; firstly we cut and resized our images into 400*320 pixels. Secondly, we detected edges using our improved HED network. Finally, the contour of an object is found based on edge detecting in the previous stage. We have significantly decreased time spent by reducing 5 side output layers to only 1 and replacing the fusion layer with a refinement and image processing module which also helps with the result. The experimental results show that our algorithm outperforms the state-of-the-art regarding images with noise and weak boundary.
Multibeam Microstrip Patch Antennas Excited by Parallel‐Plate Beam‐Forming Network With Shaped Reflecting Boundary and Optimized Slot Feeding Transition Structures

This paper presents the design of a multibeam phased array of microstrip patch antennas at 38 GHz with a parallel‐plate beam‐forming network (BFN). This antenna set, including the antenna elements and BFN, is equivalently triple‐folded into two stacked printed circuit board substrates for a compact design of nearly 60% size reduction in comparison to conventional implementation on a single planar dielectric substrate. In particular, the BFN employs a shaped metal reflecting boundary in the bottom substrate to transform the cylindrical waves of feed excitations into planar ones for a relatively linear phase variation to feed the column subarrays on the top substrate. A slot feeding transition structure is designed to couple the energy from the BFN to feed the microstrip patch antenna ports. These two mechanisms are then used to compensate the phase distortions due to BFN cavity disturbance and mutual coupling from subarrays of antennas at millimeter wave frequencies. Numerical techniques to optimize both the metal reflecting boundary and slot feeding transition structure are introduced to minimize phase distortions. Numerical and experimental results are presented to demonstrate the radiation characteristics of the integrated antenna set.
Combinatorial algorithms for perturbation theory and application on quantum computing

Cao, Yudong Ph.D., Purdue University, December 2016. Combinatorial Algorithms for Perturbation Theory and Application on Quantum Computing. Major Professor: Sabre Kais. This dissertation concerns the problem of simulating arbitrary quantum manybody interactions using realistic two-body interactions. To address this issue, a general class of techniques called perturbative reductions (or perturbative gadgets) is adopted from quantum complexity theory and in this dissertation these techniques are improved for experimental considerations. The idea of perturbative reduction is based on the mathematical machinery of perturbation theory in quantum physics. A central theme of this dissertation is then to analyze the combinatorial structure of the perturbation theory as it is used for perturbative reductions. Specifically, the original contribution of this dissertation is three-fold: 1. Improvement over existing perturbative reductions [7–9] by reducing the resources needed for realizing them [1]. 2. Proposal of a new perturbative reduction [2] that circumvents the need for strong interaction in almost all existing constructions, providing a more practical alternative for realizing many-body simulation. Here we go beyond the usual regime of convergence for perturbation theory where the perturbation as a whole is much smaller than the unperturbed section. 3. An efficient algorithm for computing tight upper bounds to perturbation series at arbitrary order [4]. The algorithm deals with a much broader class of physical settings and treats the combinatorial structure of perturbative expansion in much greater generality than the specific analyses for perturbative reductions.
Student responses to chain rule problems in thermodynamics

Students often struggle with the many partial derivatives used in the study of thermodynamics. This project explores how students respond to chain rule problems in an upper-level undergraduate thermodynamics course. This project’s dataset is composed of anonymized student responses to two such problems . We used an emergent coding method to sort responses by solution method . Observed solution methods include variable and differential substitution, implicit differentiation, differential division, and chain rule diagrams . The change of students’ solution methods between as signments was also observed. Responses were later analyzed to identify conceptual errors . Students make specific errors that provide insight into their lack of conceptual understanding of the solution methods.
Results from an improved flowing liquid lithium limiter with increased flow uniformity in high power plasmas in EAST

Significant engineering and physic progress was made by the upgrade of a flowing liquid lithium limiter (FLiLi) inserted into EAST H-mode discharges in 2016. The progress includes an improvement of liquid Li coverage uniformity, no macroscopic surface erosion by plasma materials interaction, effective removal of 55% of the discharge heat flux using high pressure He cooling during ohmic discharges, and the demonstrated ability to restart Li flow for a second experiment performed a week after the first. With increasing Li flow rate, fuel particle recycling and Fe impurity radiation gradually decreased, and plasma performance slightly improved, but there was no impact on the edge tungsten influx. No obvious Li bursts appeared during operation with up to about 4.5 MW auxiliary heating power with peak heat flux reaching ~4 MW m−2 on FLiLi. In addition, a gradual mitigation of edge localized mode (ELM) activity was observed with FLiLi operation. Finally, short-lived ELM-free phases were observed for the first time in EAST during FLiLi insertion, with increasing τE and transient H98(y, 2) < 2.