Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION OF THE NON-PHARMACOLOGICAL METHODS OF CONTROLLING PAIN DURING CHILDBIRTH; USING ANALYTICAL EVALUATION MODELS

Background of Study : Pain is an experience capable of causing tissue damage, it is associated with discomfort and unpleasant emotional and sensory experiences. Childbirth is usually accompanied by severe pain, fear, and anxiety, these factors can stimulate the sympathetic system to increase the release of catecholamines (adrenaline and nor-adrenaline) which can lead to the disruption of the secretion of oxytocin and slow contractions, stop it or even make it irregular. Pain during labor is regarded as one of the most severe forms of pain that can ever be experienced hence, the need for supportive approaches that are effective enough for the reduction of pain during labor. The nonpharmacologic approaches are very effective and more satisfactory methods for controlling pain during labor. Research has shown that women prefer these nonpharmacologic approaches due to the dangers that are associated with the usual pharmacologic approaches and their effects on mothers and neonates during and after delivery. The aim of this research is to evaluate and compare the various nonpharmacological approaches to reducing pain during childbirth, and to rank them according to their levels of preference based on selected criteria and assigned weights of importance using one of the multi-criteria decision-making (MCDM) technique called, the fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE). Result: The results of this study using the fuzzy PROMETHEE technique indicated that the most effective nonpharmacologic approach for reducing pain was Acupressure with a net flow of 0.0640, followed by Hydrotherapy (Sterile water injection) with a net flow of 0.0252, followed by Acupuncture with a net flow of 0.0201, and then Hypnobirthing with a net flow of 0.0179. The least preferred approach for reducing pain during labor from this evaluation is Transcutaneous Electrical Nerve Stimulation (TENS) with a negative net flow of -0.0457 based on the chosen criteria/parameters of the alternatives and the importance weights given by the experts. Conclusion: The outcome of this decision-making technique on the treatment approach to reduce pain during labor will help concerned parties such as pregnant mothers, gynecologists, midwives, healthcare supportive units, and hospital management to make an effective decision when the need arises. The fuzzy PROMETHEE technique was proposed to solve the problems associated with identifying the preferred approach for reducing pain during childbirth.
The occurrence and properties of disks around young stars

The paper discusses the occurrence and properties of disks around young stars, emphasizing in particular how these may relate to planet formation and the evolution of the solar system. The global properties of such disks often resemble those attributed to the primitive solar nebula, suggesting that conditions appropriate for planet formation commonly accompany the birth of low-mass stars. Disk masses, between 0.001 and 1 solar mass, are generally lower than those of the stars, and may represent only a fraction (less than about 10 percent) of the total system mass. From the paucity of near-IR radiation from some disks it is inferred that the inner regions there are gaps where the opacity from small particles becomes vanishingly small. Evidence is presented to the effect that gaps in the inner disks develop preferentially in the oldest objects, suggesting that, with time, matter is lost or accumulates into large particles such as planetesimals, which cannot yet be detected.
Numerical simulations of loop quantum Bianchi-I spacetimes

Due to the numerical complexities of studying evolution in an anisotropic quantum spacetime, in comparison to the isotropic models, the physics of loop quantized anisotropic models has remained largely unexplored. In particular, robustness of bounce and the validity of effective dynamics have so far not been established. Our analysis fills these gaps for the case of vacuum Bianchi-I spacetime. To efficiently solve the quantum Hamiltonian constraint we perform an implementation of the Cactus framework which is conventionally used for applications in numerical relativity. Using high performance computing, numerical simulations for a large number of initial states with a wide variety of fluctuations are performed. Big bang singularity is found to be replaced by anisotropic bounces for all the cases. We find that for initial states which are sharply peaked at the late times in the classical regime and bounce at a mean volume much greater than the Planck volume, effective dynamics is an excellent approximation to the underlying quantum dynamics. Departures of the effective dynamics from the quantum evolution appear for the states probing deep Planck volumes. A detailed analysis of the behavior of this departure reveals a non-monotonic and subtle dependence on fluctuations of the initial states. We find that effective dynamics in almost all of the cases underestimates the volume and hence overestimates the curvature at the bounce, a result in synergy with earlier findings in the isotropic case. The expansion and shear scalars are found to be bounded throughout the evolution.
H/sub /spl infin// control for impulsive disturbances: a state-space solution

Formulates and studies a (sub)optimal H/sub /spl infin// control problem related to the attenuation of impulsive disturbances to a class of linear systems. The control objective is to design a stabilizing controller to minimize the induced operator norm from the impulsive disturbances to the controlled outputs. The authors derive necessary and sufficient conditions for the existence of a (sub)optimal controller, and give a procedure to compute such a controller when one exists.
Quality indicators on medicines packaging as guide to routine quality assurance for procured generic medicines: A community pharmacy survey in Nigeria

Introduction: While medicines quality assurance is best performed by a Stringent Regulatory Authority (SRA), many countries still lack this capacity. In these contexts, brand manufacturers take additional steps to signal authenticity to their consumers through the use of overt authentication technologies on medicines packaging. The aim of this study was to use quality indicators on the secondary packaging of medicines to identify, and, thus, risk rank, therapeutic groups most prone to being substandard or falsified. Method: This study profiled medicines in the inventory of a conveniently selected pharmacy in Nigeria by three common packaging quality indicators: (i) National Agency for Food and Drug Administration, NAFDAC, registration number, (ii) a mobile authentication service, MAS, number, and (iii) a hologram. Results: The inventory contained 66 medicines available as 380 oral, parenteral, or topical products. Of these, 92% had a NAFDAC number, 20% had a MAS number, and 10% bore a hologram, either exclusively, or simultaneously. Antimicrobials, mostly antibiotics and antimalarials, were the most common therapeutic group containing one or all three quality indications. Antibiotic medicines for children formulated as suspensions and drops had the most quality assurance indicators on their packaging. Conclusion: This result suggests that antimicrobials, especially those formulated for use in children, may be the medicine group at the greatest risk of being substandard or falsified (SF) in Nigeria. The implication of this finding is the need for healthcare facilities to implement strict medicines quality assurance procurement practices especially for antimicrobials. Continued vigilance by the public would also be necessary.
Genotypic Characterization of Urinary Tract Infections causing bacteria isolates among Adults at Kiambu Level 5 Hospital, Kenya: Selected Extended Spectrum Î²-Lactamase genes and Biofilm Formation

The menace of antimicrobial resistance to public health is constantly arising globally. Many pathogenic bacteria use mechanisms such as mutations and biofilm formation, which significantly reduces efficacy of antimicrobial agents. In this cross-sectional study, we aimed at determining the prevalence of selected extended spectrum {beta}-lactamase (ES{beta}Ls) genes and analyse the possible biofilm formation abilities of the isolated bacteria causing urinary tract infection among adult patients seeking medicare at Kiambu Level 5 hospital, Kenya. The double-disk synergy test was used for phenotypic identification of ES{beta}Ls producing isolates, while microtiter plate assays with some modifications were used to test biofilm formation analysis. A total of 10 isolates were bioassayed for ES{beta}L genes presence out of 57 bacteria isolates obtained from urine samples. From this study, the blaTEM genes were found to be the most prevalent ES{beta}Ls genes (100%), followed by blaOXA and blaSHV genes at 40% and 30% respectively. In addition, the co-carriage of blaTEM and blaSHV was revealed at 50% lower than that of blaTEM + blaOXA genes at 66.7% among the study E. coli isolates. Biofilm formation finding disclosed that most of the isolates form biofilms 36 (63.2%), with Gram-negatives being the most biofilm formers 25 (69.4%) compared to the Gram-positive 11 (30.6%). E. coli 15(41.7%), Klebsiella sp. 7(19.4%) and S. aureus 7(19.4%) were the most common biofilm formers. Further analysis showed no significant difference in biofilm formation among all tested isolates with a p-value of more than 0.05. However, overall Gram-positive isolates had a significant P-value of 0.056. Although biofilm formation's impact on urinary tract infections is not fully recognized, the carriage of ES{beta}Ls resistance genes and the biofilm formation ability negatively impact effectiveness of UTI treatment. Therefore, we advocate for surveillance studies to map ES{beta}Ls distribution and biofilm formation genes among UTI etiological agents to halt UTI treatment failure.
Surface electronic structure and magnetic properties of doped manganites

The electronic structure and magnetic properties of doped manganites surfaces are investigated. It is assumed that, at the outermost layer, the environment of the Mn ions does not have cubic symmetry. The ${e}_{g}$ orbitals are split and the double exchange mechanism is weakened. The charge state of the Mn ions is modified, and the magnetic ordering of the spins tends to be antiferromagnetic. The surface magnetization and the dependence of the transport properties through the resulting surface barrier on applied magnetic field and temperature is analyzed.
TRGB Distances to Galaxies in Front of the Virgo Cluster

Tip of the red giant branch distances are acquired from Hubble Space Telescope images for 16 galaxies to the foreground of the Virgo Cluster. The new distances with 5% accuracy, combined with archival measurements, tightly constrain the near-side location of the onset of infall into the Virgo Cluster to be 7.3 ± 0.3 Mpc from the cluster, reaching within 9 Mpc of the Milky Way. The mass within this turnaround radius about the cluster is (8.3 ± 0.9) × 1014 M⊙. Color–magnitude diagrams are provided for galaxies in this study and there is a brief discussion of their group affiliations.
Polarized Sunyaev Zel'dovich tomography

Secondary CMB polarization is induced by the late-time scattering of CMB photons by free electrons on our past light cone. This polarized Sunyaev Zel'dovich (pSZ) effect is sensitive to the electrons' locally observed CMB quadrupole, which is sourced primarily by long wavelength inhomogeneities. By combining the remote quadrupoles measured by free electrons throughout the Universe after reionization, the pSZ effect allows us to obtain additional information about large scale modes beyond what can be learned from our own last scattering surface. Here we determine the power of pSZ tomography, in which the pSZ effect is cross-correlated with the density field binned at several redshifts, to provide information about the long wavelength Universe. The signal we explore here is a power asymmetry in the cross-correlation between E or B mode CMB polarization and the density field. We compare this to the cosmic variance limited noise: the random chance to get a power asymmetry in the absence of a large scale quadrupole field. By computing the necessary transfer functions and cross-correlations, we compute the signal-to-noise ratio attainable by idealized next generation CMB experiments and galaxy surveys. We find that a signal-to-noise ratio of ∼ 1−10 is in principle attainable over a significant range of power multipoles, with the strongest signal coming from the first multipoles in the lowest redshift bins. These results prompt further assessment of realistically measuring the pSZ signal and the potential impact for constraining cosmology on large scales.
Rotationally equivariant super-resolution of velocity fields in two-dimensional flows using convolutional neural networks

This paper investigates the super-resolution of velocity fields in two-dimensional flows from the viewpoint of rotational equivariance. Super-resolution refers to techniques that enhance the resolution of an image from low to high resolution, and it has recently been applied in fluid mechanics. Rotational equivariance of super-resolution models is defined as the property by which the super-resolved velocity field is rotated according to a rotation of the input, leading to inferences that are covariant with the orientation of fluid systems. In physics, covariance is often related to symmetries. To better understand the connection with symmetries, the notion of rotational consistency of datasets is introduced within the framework of supervised learning, which is defined as the invariance of pairs of low- and high-resolution velocity fields with respect to rotation. This consistency is sufficient and necessary for super-resolution models to learn rotational equivariance from large datasets. Such a large dataset is not required when rotational equivariance is imposed on super-resolution models through the use of prior knowledge in the form of equivariant kernel patterns. Nonetheless, even if a fluid system has rotational symmetry, this symmetry may not carry over to a velocity dataset, which is not rotationally consistent. This inconsistency can arise when the rotation does not commute with the generation of low-resolution velocity fields. These theoretical assertions are supported by the results of numerical experiments, where two existing convolutional neural networks (CNNs) are converted into rotationally equivariant CNNs and the inferences of these CNNs are compared after the supervised training.
Studying the Diophantine problem in finitely generated rings and algebras via bilinear maps

We study systems of polynomial equations in several classes of ﬁnitely generated rings and algebras. For each ring R (or algebra) in one of these classes we obtain an interpretation by systems of equations of a ring of integers O of a ﬁnite ﬁeld extension of either Q or F p p t q , for some prime p and variable t . This implies that the Diophantine problem (decidability of systems of polynomial equations) in O is Karp-reducible to the same problem in R . In several cases we further obtain an interpretation by systems of equations of the ring F p r t s in R , which implies that the Diophantine problem in R is undecidable in this case. Otherwise, the ring O is a ring of algebraic integers, and then the long-standing conjecture that Z is always interpretable by systems of equations in a ring of algebraic integers carries over to R . If true, it implies that the Diophantine problem in R is also undecidable. Some of the classes of ﬁnitely generated rings studied in this paper are the following: all associative, commutative, non-unitary rings (a similar statement for the unitary case was obtained by Eisentraeger); all possibly non-associative, non-commutative non-unitary rings that are ﬁnitely generated as an abelian group; and several classes of ﬁnitely generated non-commutative rings. Analogous statements are obtained for algebras over ﬁnitely generated associative commutative unitary rings. Another contribution of the paper is the technique by which the aforementioned results are obtained. More precisely, we show that given a bilinear map f : A ˆ B Ñ C between ﬁnitely generated abelian groups (or modules), under some mild assumptions, there exists a certain ring (or algebra) R with nice properties which is interpretable by systems of equations in the multi-sorted structure p A, B, C ; f q . This result ﬁts nicely the study of rings (or algebras) since the multiplication operation of such structures can be seen as a bilinear map between abelian groups (or modules). This result is potentially applicable in many other settings, such as in the area of group theory, see for example [11].
Structure-based Drug Design with Equivariant Diffusion Models

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. In this paper, we formulate SBDD as a 3D-conditional generation problem and present DiffSBDD, an SE(3)-equivariant 3D-conditional diffusion model that generates novel ligands conditioned on protein pockets. Comprehensive in silico experiments demonstrate the efficiency and effectiveness of DiffSBDD in generating novel and diverse drug-like ligands with competitive docking scores. We further explore the flexibility of the diffusion framework for a broader range of tasks in drug design campaigns, such as off-the-shelf property optimization and partial molecular design with inpainting.
Decay dynamics in the coupled-dipole model

Abstract Cooperative scattering in cold atoms has gained renewed interest, in particular in the context of single-photon superradiance, with the recent experimental observation of super- and subradiance in dilute atomic clouds. Numerical simulations to support experimental signatures of cooperative scattering are often limited by the number of dipoles which can be treated, well below the number of atoms in the experiments. In this paper, we provide systematic numerical studies aimed at matching the regime of dilute atomic clouds. We use a scalar coupled-dipole model in the low excitation limit and an exclusion volume to avoid density-related effects. Scaling laws for super- and subradiance are obtained and the limits of numerical studies are pointed out. We also illustrate the cooperative nature of light scattering by considering an incident laser field, where half of the beam has a phase shift. The enhanced subradiance obtained under such condition provides an additional signature of the role of coherence in the detected signal.
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation

Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available. Despite increasing interest in automatic fact-checking, it is still unclear if automated approaches can realistically refute harmful real-world misinformation. Here, we contrast and compare NLP fact-checking with how professional fact-checkers combat misinformation in the absence of counter-evidence. In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims. We then define two requirements that the evidence in datasets must fulfill for realistic fact-checking: It must be (1) sufficient to refute the claim and (2) not leaked from existing fact-checking articles. We survey existing fact-checking datasets and find that all of them fail to satisfy both criteria. Finally, we perform experiments to demonstrate that models trained on a large-scale fact-checking dataset rely on leaked evidence, which makes them unsuitable in real-world scenarios. Taken together, we show that current NLP fact-checking cannot realistically combat real-world misinformation because it depends on unrealistic assumptions about counter-evidence in the data.
Practical Program Repair in the Era of Large Pre-trained Language Models

Automated Program Repair (APR) aims to help developers automatically patch software bugs. However, current state-of-the-art traditional and learning-based APR techniques face the problem of limited patch variety, failing to fix complicated bugs. This is mainly due to the reliance on bug-fixing datasets to craft fix templates or directly predict potential patches. Large Pre-Trained Language Models (PLMs), trained using billions of text/code tokens, can potentially help avoid this issue. Very recently, researchers have directly leveraged PLMs for APR without relying on any bug-fixing datasets. Meanwhile, such existing work either failed to include state-of-the-art PLMs or was not evaluated on realistic datasets. In this work, we perform the first extensive study on directly applying PLMs for APR. We select 9 recent state-of-the-art PLMs, including both generative and infilling models, ranging from 125M to 20B in size. We designed 3 different repair settings to evaluate the different ways we can use PLMs to generate patches. We apply the PLMs under these repair settings on 5 datasets across 3 different languages and compare different PLMs in the number of bugs fixed, generation speed and compilation rate. Our study demonstrates that directly applying state-of-the-art PLMs can already substantially outperform all existing APR techniques on all our datasets. Among the studied PLMs, the scaling effect exists for APR where larger models tend to achieve better performance. Also, we show for the first time that suffix code after the buggy line (adopted in infilling-style APR) is important in not only generating more fixes but more patches with higher compilation rate. Besides patch generation, the PLMs consider correct patches to be more natural than other ones, and can even be leveraged for effective patch ranking or patch correctness checking.
Characterization of singular flows of zeroth-order pseudo-differential operators via elliptic eigenfunctions: a numerical study

The propagation of internal gravity waves in stratified media, such as those found in ocean basins and lakes, leads to the development of geometrical patterns called"attractors". These structures accumulate much of the wave energy and make the fluid flow highly singular. In more analytical terms, the cause of this phenomenon has been attributed to the presence of a continuous spectrum in some nonlocal zeroth-order pseudo-differential operators. In this work, we analyze the generation of these attractors from a numerical analysis perspective. First, we propose a high-order pseudo-spectral method to solve the evolution problem (whose long-term behaviour is known to be not square-integrable). Then, we use similar tools to discretize the corresponding eigenvalue problem. Since the eigenvalues are embedded in a continuous spectrum, we compute them using viscous approximations. Finally, we explore the effect that the embedded eigenmodes have on the long-term evolution of the system.