Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Nursing Guidelines on Hemorrhoids Symptoms among Pregnant Women

Background: Hemorrhoids (also known as ‘piles’) are swollen varicose veins located around the anus. Hemorrhoids are a common condition in pregnancy. The main reported symptoms caused by hemorrhoids are burning, itching, perianal pain, and bleeding Aim : The study aimed to assess the effect of nursing guidelines on hemorrhoids symptoms among pregnant women . Design: A Quasi experimental design was used for conducting the study. Setting : The study was conducted in the obstetrics and gynecology antenatal outpatient clinic at Benha University Hospital. Sample : A purposive sample of 120 pregnant women divided to 2 equal groups study group and control group were selected according to inclusion criteria. Tools: two tools were utilized for data collection 1) A structured interviewing questionnaire 2) The Colorectal Evaluation of Clinical Therapeutics Scale. Results of this study showed that knowledge and healthy practices there was insignificant relation between study and control groups pre intervention guidelines while high statistically significant relation between both groups post intervention guidelines, also high statistically significant correlation between knowledge and healthy practice with severity of hemorrhoidal symptoms post intervention in both groups. Conclusion: significant improvement knowledge, healthy practice, and significant improvement of severity hemorrhoids symptoms post intervention guidelines. Recommendations: health education program regarding general heathy practice during pregnancy and dietary education program during antenatal visits. Replication of the current study on a large representative sample to achieve generalization of the results.
Set-Membership State Estimation for 2-D Roesser Systems Based on Zonotope Radius and Its Application

This brief is concerned with the problem of zonotopic set-membership state estimation for 2-D Roesser systems subjected to unknown-but-bounded disturbance. First, a general guaranteed state estimation algorithm is designed to obtain a set of zonotopes containing the system states that are consistent with the model parameters and the measurement outputs. Then, based on the constructed <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>-radius functions, an optimal correction matrix design criterion with <inline-formula> <tex-math notation="LaTeX">${L}_{\infty }$ </tex-math></inline-formula> performance is provided for the obtained zonotope. Furthermore, an offline optimization problem is constructed to obtain the optimal correction matrix. Finally, a numerical example is provided to certify the effectiveness of the proposed approach.
Sparse Functional Dynamical Models—A Big Data Approach

ABSTRACT Nonlinear dynamical systems are encountered in many areas of social science, natural science, and engineering, and are of particular interest for complex biological processes like the spiking activity of neural ensembles in the brain. To describe such spiking activity, we adapt the Volterra series expansion of an analytic function to account for the point-process nature of multiple inputs and a single output (MISO) in a neural ensemble. Our model describes the transformed spiking probability for the output as the sum of kernel-weighted integrals of the inputs. The kernel functions need to be identified and estimated, and both local sparsity (kernel functions may be zero on part of their support) and global sparsity (some kernel functions may be identically zero) are of interest. The kernel functions are approximated by B-splines and a penalized likelihood-based approach is proposed for estimation. Even for moderately complex brain functionality, the identification and estimation of this sparse functional dynamical model poses major computational challenges, which we address with big data techniques that can be implemented on a single, multi-core server. The performance of the proposed method is demonstrated using neural recordings from the hippocampus of a rat during open field tasks. Supplementary materials for this article are available online.
The Relationship Between Active Galactic Nuclei and Metal-enriched Outflows in Galaxy Clusters

Clusters of galaxies are host to powerful Active Galactic Nuclei (AGN) that greatly affect the thermal history of clusters. By keeping X-ray emitting gas from cooling, massive, run away star formation does not occur in the brightest cluster galaxy (BCG). This is achieved through radio jets displacing large quantities of metal-rich gas and carving out cavities in the intracluster medium (ICM). This metal-rich gas was originally formed within the BCG and ejected through type Ia supernovae. The current distribution of the ejecta suggests an extra source of energy has spread the material far out into the ICM. Currently, it is unclear what mechanisms are responsible. In this thesis, I present evidence, in the form of X-ray imaging and spectra, that establishes a link between AGN and the observed distribution of metal-rich gas. First, the BCG in the Abell 1664 cluster is unusually blue and is forming stars at a rate of ∼ 23 M yr−1. The BCG is located within 5 kpc of the X-ray peak, where the cooling time of 3.5×10 yr and entropy of 10.4 keV cm are consistent with other star-forming BCGs in cooling flow clusters. The cooling rate in this region is roughly consistent with the star formation rate, suggesting that the hot gas is condensing onto the BCG. We use the scaling relations of B̂ırzan et al. (2008) to show that the AGN is underpowered compared to the central X-ray cooling luminosity by roughly a factor of three. We suggest that A1664 is experiencing rapid cooling and star formation during a low-state of an AGN feedback cycle that regulates the rates of cooling and star formation. Modeling the emission as a single temperature plasma, we find that the metallicity peaks 100 kpc from the X-ray center, resulting in a central metallicity dip. However, a multi-temperature cooling flow model improves the fit to the X-ray emission and is able to recover the expected, centrally-peaked metallicity profile. Next, using deep Chandra observations of the Hydra A galaxy cluster, we examine the metallicity structure near the central galaxy and along its powerful radio source. We show that the metallicity of the ICM is enhanced by up to 0.2 dex along the radio jets and lobes compared to the metallicity of the undisturbed gas. The enhancements extend from a radius of 20 kpc from the central galaxy to a distance of ∼ 120 kpc. We estimate the total iron mass that has been transported out of the central galaxy to be between 2×10 M and 7×10 M which represents 10% 30% of the iron mass within the central galaxy. The energy required to lift this gas is roughly 1% to 5% of the total energetic output of the AGN. Evidently, Hydra A’s powerful radio source is able to redistribute metal-enriched, low entropy gas throughout the core of the galaxy cluster. The short re-enrichment timescale < 10 yr implies that the metals lost from the central galaxy will be quickly replenished.
3D MHD modelling of the glidarc behaviour of a low current – high voltage DC plasma torch

We present in this paper a MHD modelling of the gliding arc behaviour of a DC plasma torch operating with air under low current and high voltage conditions. The low current leads to instabilities and difficulties to simulate the process because the magnetic field is not sufficient to constrict the arc. The model is 3D, time-dependent and the MHD equations are solved in using CFD software Code_Saturne ® . Although the arc is definitively non-LTE, the LTE assumption is considered as a first approach. The injection of air is tangential. A hot gas channel reattachment model has been used to simulate the restriking process of the arc root. After the description of the model, the most appropriate electrical voltage breakdown parameter has been selected in comparing with experimental results. A typical operating point is then studied in details and shows the helical shape of the arc discharge in the nozzle. Finally, the mass flow rate and the current have been varied in the range 0.16 – 0.5 gs -1 and 100 – 300 mA, respectively, corresponding to typical glidarc operating points of our experimental plasma torch. The model shows good consistency with experimental data in terms of: global behaviour, arc length, mean voltage and glidarc frequency.
Plans for an accurate alignment of the VLBI frame and the future Gaia frame

The European space astrometry mission Gaia will construct a dense optical QSO-based celestial reference frame. For consistency between optical and radio positions, it will be fundamental to align the Gaia and VLBI frames with the highest accuracy. A proper alignment is also important in the framework of astrophysics, for example to probe properly the AGN jets properties and the physics of these objects. The VLBI-Gaia frame alignment requires quasars that are bright at optical wavelength, that have a compact radio core, and that do not exhibit complex structures. In this paper, we draw prospects for this alignment, based on the ICRF2 catalogue and an ongoing dedicated VLBI project designed to observe additional weaker extragalactic radio sources for this purpose. The list of suitable sources will have to be monitored to check the relevance of the sources for the alignment, especially in terms of position stability and structures. Accordingly, we present the observations we envision in the framework of the IVS and other VLBI networks, before and during the Gaia mission.
Endurance of Medium Voltage Joints under Laboratory Wet Aging Environments

Traditionally, when Medium voltage (MV) power cable accessories (joints and terminations) are tested in a laboratory environment, they are assessed as stand-alone units. This is expected since the main goal of the laboratory testing is to assess the performance of the accessory design and not the long term interactions of the system. In this case, long term interactions (>100 days) between the cable and accessory are non-existent; understanding the interactions is important because they better relate to a more realistic performance of a power cable system installed in the field. Therefore, the work presented here examined the possibility of testing cable joints using a laboratory test method based on that used for cable qualification and comparing the results to those of cable alone. It also developed a method to perform a relative comparison of joint and cable interaction with relevance to field circuits.
Fuzztruction: Using Fault Injection-based Fuzzing to Leverage Implicit Domain Knowledge

Today’s digital communication relies on complex protocols and speciﬁcations for exchanging structured messages and data. Communication naturally involves two endpoints: One generating data and one consuming it. Traditional fuzz testing approaches replace one endpoint, the generator , with a fuzzer and rapidly test many mutated inputs on the target program under test. While this fully automated approach works well for loosely structured formats, this does not hold for highly structured formats, especially those that go through complex transformations such as compression or encryption. In this work, we propose a novel perspective on generating inputs in highly complex formats without relying on heavy-weight program analysis techniques, coarse-grained grammar approximation, or a human domain expert. Instead of mutating the inputs for a target program, we inject faults into the data generation program so that this data is almost of the expected format. Such data bypasses the initial parsing stages in the consumer program and exercises deeper program states, where it triggers more interesting program behavior. To realize this concept, we propose a set of compile-time and run-time analyses to mutate the generator in a targeted manner, so that it remains intact and produces semi-valid outputs that satisfy the constraints of the complex format. We have implemented this approach in a prototype called F UZZ - TRUCTION and show that it outperforms the state-of-the-art fuzzers AFL++, S YM CC, and W EIZZ . F UZZTRUCTION ﬁnds signiﬁcantly more coverage than existing methods, especially on targets that use cryptographic primitives. During our evaluation, F UZZTRUCTION uncovered 151 unique crashes (after automated deduplication). So far, we manually triaged and reported 27 bugs and 4 CVEs were assigned.
A g-C3N4/PVDF-based composite polymer electrolytes for sodium-ion battery

PVDF based polymer solid electrolytes are one of the most promising choices for all-solid-state sodium ion batteries and sodium metal batteries. However, neat polymer solid electrolytes suffered from relatively low ionic conductivity at room temperature, narrow electrochemical stability window. In this work, g-C 3 N 4 nanosheets are introduced as a novel nano�ller for PVDF-HFP based composite solid polymer electrolytes. The g-C 3 N 4 �ller can engineer the PVDF-HFP structure, resulting in reduced decreased crystallinity. Additionally, the surface atoms of the g-C 3 N 4 interact with groups in the sodium salt, promoting further dissociation of the sodium salt. The incorporation of g-C 3 N 4 improves the electrical properties (ionic conductivity, Na + transference number and electrochemical window), mechanical properties and thermal stability of the composite electrolyte. The composite electrolyte shows a low Na deposition/dissolution overpotential of about 100 mV at a current density of 1 mA cm − 2 after 160 cycles. The sodium metal battery with g-C 3 N 4 composite electrolyte and NVP cathode exhibits lower polarization voltage (90 mV), and stable reversible capacity of 93 mAh g − 1 after 200 cycles at 1C.
The virtual knife

Since post World War II and until 2008 the Danish pig producing industry (DPPI) has been in a continuing state of growth. In spite of an ever fiercer competition DPPI has managed to protect its position as export leader by maintaining a focus on research and development. Today, DPPI is in a state of recession and must increase the efficiency if not to reduce the production capacity further. The industry recognizes that a more efficient use of the raw materials is one of the largest and most important challenges. To meet this challenge it is a necessity to get a better understanding of the biological variation of pigs. The development of models for describing the biological variation of pigs is one of the key components needed to attain a better sorting of the pig carcasses and an improved cutting in the abattoirs. Such models can be related to possible products, which can be related to potential yield and order books. The Danish Meat Research Institute (DMRI) is currently constructing a representative database of virtual representations of pigs using X-ray Computed Tomography (CT). The database will serve as the foundation for the diversity modeling of pigs and for extracting predictors of quality and optimal use. This thesis integrates well-known techniques from the medical image analysis into the development of tools for automated analysis of the morphology of pigs. E.g. elastic image matching has been applied to establish spatial correspondence between the virtual representations of pigs in the database. The establishment of spatial correspondence is an essential preprocessing step for most automated analysis using the database.
Enrichment set cover problem

As a common task in the fields of bioinformatics, enrichment analysis aims to investigate the functional association between a gene list of interest which often derived from biological experiments, and specific gene sets in a large database. The core problem of enrichment analysis can be characterized as a two-objective optimization problem. In this paper, we formulated the multiple gene sets enrichment problem into a variant of the set cover problem, named enrichment set cover problem, and designed four approximation algorithms to solve the NP-hard enrichment set cover problem. The performance of proposed approximation algorithms were analyzed theoretically and evaluated on the data sets from real biological databases.
A global social activation model of enabling conditions for positive social tipping - the role of sea-level rise anticipation and climate change concern

Effective climate change mitigation necessitates swift societal transformations. Social tipping processes, where small triggers initiate qualitative systemic shifts, are potential key mechanisms instigating societal change. A necessary foundation for societal tipping processes is the creation of enabling conditions. Here we assess future sea-level rise estimates and social survey data within the framework of a social activation model to exemplify the enabling conditions for tipping processes. We find that in many countries, climate change concern is sufficient, the enabling conditions and opportunities for social activation 5 already exist. Further, drawing upon the interrelation between climate change concern and anticipation of future sea level rise, we report three qualitative classes of tipping potential that are regionally clustered, with greatest potential for tipping in Western Pacific rim and East Asian countries. These findings propose a transformative pathway where climate change concern increases the social tipping potential, while extended anticipation time horizons can trigger the system towards an alternative trajectory of larger social activation for climate change mitigation
Solving the Issue of Finding Differences in an Illusory Context

Introduction . There are two opposite theoretical descriptions of visual perception: constructivist and ecological. To solve the issue of whether the perception of visual illusions is the result of a decision or whether such perception is natural and can be described using an ecological approach, an experiment was conducted to find differences in an illusory and nonillusory context, which reflects the novelty of this study. Methods. The subjects were offered a series of paired images in which it was necessary to find the difference as quickly as possible. The images could be the same: One picture could be 10 % larger than another, or, due to the illusory context, one picture could seem larger than another, and the image size was subjectively distorted due to the illusion of Ponzo and Delboeuf. According to the instructions, the size difference (real or apparent) should be ignored, and it was necessary to look for other differences. The time and precision of the response were measured for each option. Results and Discussion . Significant differences in visual field search time were found for images with an illusory difference, a real difference in size, and identical ones. For the first time, it was found that the problem is solved more efficiently (faster and more accurately) in the presence of two exact images, and the lowest efficiency is observed when solving a problem with an illusory difference in the size of images. It is concluded that the illusory context has an additional inhibitory effect on the process of solving the problem of finding differences. The advantage of describing the perception of visual illusions using a constructivist approach is experimentally shown.
INVESTIGATING IMPROVEMENTS TO MESH INDEXING

The MEDLINE database currently comprises an extensive collection of over 35 million citations, with more than 1 million records being added each year [28]. The abundance of information available in the database presents a significant challenge in identifying and locating relevant research articles on a given search topic. This has prompted the development of various techniques and approaches aimed at improving the efficiency and effectiveness of information retrieval from the MEDLINE database. A search engine devoted to the research publications on MEDLINE is called PubMed. MeSH, or Medical Subject Headings, is a restricted vocabulary used by PubMed to categorize each article. Human annotators have been used for decades, which is not only time-consuming but also expensive. Due to its enormously complex hierarchically ordered structure, MeSH indexing is a difficult problem in the machine learning domain. We propose a model which addresses all these challenges. We propose an end-to-end model that takes MeSH description into account and combines it with a Knowledge Enhanced Mask attention model to index new research papers. We also calculated the journal correlation of each MeSH term in the MeSH hierarchy.
Binarization of uneven-lighting image by maximizing boundary connectivity

Abstract By using the constraint of boundary connectivity, a new technique for binarizing a gray level image is proposed. Initially, the gray level image is partitioned into a number of fixed-size subimages and the best binarization of each subimage is estimated. Of all subimages, the one with the best binarization effect is first binarized to be the starting binary area of the gray level image. Next, of the subimages neighboring the binary area, the one with the best binarization effect is selected to perform the following test. If the maximal boundary connectivity achieved by binarizing the selected subimage with a specific threshold satisfies the constraint of boundary connectivity, the subimage will thus be binarized to update the partially-binarized gray level image, otherwise, the subimage will be marked “ignored” and remain gray throughout the subimage binarization procedure. The procedure will be repeated until no further subimages neighboring the binary area can be binarized. If there are any gray subimages without being marked “ignored,” the one with the best binarization effect will be binarized to create a new binary area in the partially-binarized gray level image, and then the procedure of extending the binary area will be repeated again. At the last stage, the proposed method constructs a threshold surface for the original gray level image based on the binarized subimages. Experimental results show the effectiveness of this method in binarizing uneven-lighting gray level images.
Distributed Setup for Trackmania Reinforcement Learning

This report focuses on the exploration, description, and evaluation of approaches aimed at achieving scalability for rollout workers in the RL framework tmrl, which hosts the Trackmania Roborace League. The competition offers participants the opportunity to benchmark their vision-based autonomous car racing strategies on Trackmania 2020 through solving Real-Time Gym environments. However, the limitation of rollout workers being only executable on Windows machines hinders scalability and policy improvement. To address this limitation, we present a solution that enables the deployment of rollout workers on Linux-based machines. By utilizing Lutris, Trackmania is run on Linux, and an extension to the tmrl library allows seamless interaction with the game environment from a Linux system. The successful deployment of rollout workers on Linux opens up new possibilities for scaling and enhancing policy learning in the tmrl framework. The project is nearly complete, with the establishment of most components required for a fully automated pipeline to run workers on a Linux cluster. The final phase involves integrating the necessary pieces by creating a Docker container that initiates the Xvfb-server, launches Lutris, and sets up Trackmania. Fortunately, the individual scripts for these actions are functional and ready for orchestration within the Docker container. Furthermore, the tmrl-extension,