Miracle-dz/UniFAR-FTUs huggingface dataset:
Rural Nurses’ Views on Breastmilk Banking in Limpopo Province, South Africa: A Qualitative Study

The development of breastmilk banks is being established among the African population, including in Limpopo Province. However, the views of nurses directly handling the donated breastmilk in the province remain unknown. This study was aimed at exploring and describing the views of nurses towards breastmilk banking in the Mankweng area, Limpopo Province. A qualitative, descriptive, and explorative study was undertaken at a tertiary hospital and a rural feeder clinic in the Mankweng area. Purposive sampling was employed to obtain participants for the interviews. One-on-one, semi-structured interviews were conducted to explore the views of these nurses. Data were analysed using Tesch’s open coding method, with the information obtained being grouped into different themes and sub-themes. Almost all nurses were willing to donate their breastmilk to the bank. However, receiving donated breastmilk for their own children seemed to be a challenge due to the safety of the donated breastmilk, uncertainty about the screening process, and cultural issues. Although donation of breastmilk appeared to be well supported by almost all the nurses, the use of donated breastmilk seemed to be not fully acceptable. Increased awareness about breastmilk donation and banking should be prioritised in the province.
Finite-time H ∞ control of discrete-time switched systems based on transferring-dependent Lyapunov function approach

This paper studies the finite-time issues (finite-time stability, finite-time boundedness, finite-time H ∞ control) of discrete-time switched systems. Different from the existing Lyapunov function (LF) approaches only concerning activated subsystem, a novel approach for constructing LF is first proposed, which not only depends on the subsystem the switching reaches but also is closely related to the freshly inactive subsystem. Based on this, a transferring-dependent convex Lyapunov function (CLF) and a transferring-dependent LF are introduced, under which the improved finite-time stability results are deduced with admissible edge-dependent average dwell time (AED-ADT) technique. In light of the merits of established finite-time stability criteria, two transferring-dependent controllers are devised—that is, transferring-dependent convex controller (TD-CC) and transferring-dependent ordinary controller (TD-OC)—to perform finite-time control synthesis. Then, the finite-time H ∞ control is further investigated based on the obtained control synthesis results. The designed transferring–dependent controllers can guarantee that the underlying system is finite-time bounded (FTB) with a prescribed H ∞ performance under tighter AED-ADT switching signals. Besides, it is the first time to use the multiple convex Lyapunov function (MCLF) to investigate the finite-time issues. Finally, two numerical examples and a practical example are used to verify the effectiveness of the developed results.
Alteration of Ni silicide formation by N implantation

The possibility of controlling the growth of nickel silicide by implanting N into thin Ni films evaporated on Si substrates has been studied using (^4)He backscattering spectrometry. The reaction between Ni and Si is completely halted below annealing temperatures of ∼375 °C by implanted doses of 5×10(^16) N/cm^2. At higher annealing temperatures, localized intermixing takes place. For low doses ≲0.5×10(^16) N/cm^2, the reaction between Ni and Si is that observed for unimplanted samples both in the phase formed (Ni(_2)Si) and in rate of growth. For intermediate doses ∼0.9×10(^16) N/cm^2, the first phase formed corresponds to NiSi, and the growth rate is greatly reduced. These results are explained in terms of a silicon nitride barrier to Ni diffusion forming at the Ni/Si interface.
Optimal transmit antenna selection for non‐orthogonal multiple access with improved lion algorithm

In the realm of 5G mobile wireless communication, cognitive radio (CR) plays an important role in improving radio spectrum efficiency by overcoming challenges such as spectrum shortage versus under‐utilization associated with the traditional fixed frequency assignment. “Interweave CR, underlay CR, and overlay CR” are the three primary paradigmatics in general. The present study focuses on the overlay CR paradigm, wherein secondary users (SUs) have all been expected to be provided with superior “encoding and signal processing techniques” to improve primary users' (PUs') communication. Furthermore, to address concerns associated with inefficient spectrum usage, multiplexing methods like “non‐orthogonal multiple access (NOMA) and spatial modulation (SM)” have been implemented. NOMA makes use of the power domain to maximize spectrum usage, whereas SM makes use of the spatial domain. Multiple antennas, on the other hand, are expensive in terms of energy, magnitude, and equipment. Antenna selection is a low‐cost, low‐complexity method of capturing many of the NOMA system's advantages. As a consequence, this study offers the fitness familiarized lion algorithm (FFLA), a novel methodology for selecting the best antennas. Furthermore, the selection procedure involves identifying the stated objectives, which include energy usage and error minimization. Indeed, the suggested technique is an enhanced version of the lion algorithm (LA). Ultimately, the suggested work's performance is compared to and proven against other traditional models.
Developing a Feline Immunodeficiency Virus Subtype B Vaccine Prototype Using a Recombinant MVA Vector

The feline immunodeficiency virus (FIV) is a retrovirus with global impact and distribution, affecting both domestic and wild cats. This virus can cause severe and progressive immunosuppression culminating in the death of felids. Since the discovery of FIV, only one vaccine has been commercially available. This vaccine has proven efficiency against FIV subtypes A and D, whereas subtype B (FIV-B), found in multiple continents, is not currently preventable by vaccination. We, therefore, developed and evaluated a vaccine prototype against FIV-B using the recombinant viral vector modified vaccinia virus Ankara (MVA) expressing the variable region V1–V3 of the FIV-B envelope protein. We conducted preclinical tests in immunized mice (C57BL/6) using a prime-boost protocol with a 21 day interval and evaluated cellular and humoral responses as well the vaccine viability after lyophilization and storage. The animals immunized with the recombinant MVA/FIV virus developed specific splenocyte proliferation when stimulated with designed peptides. We also detected cellular and humoral immunity activation with IFN-y and antibody production. The data obtained in this study support further development of this immunogen and testing in cats.
Structure-Function Relationship of the Disintegrin Family: Sequence Signature and Integrin Interaction

Disintegrins are small cysteine-rich proteins found in a variety of snake venom. These proteins selectively modulate integrin function, heterodimeric receptors involved in cell-cell and cell-matrix interaction that are widely studied as therapeutic targets. Snake venom disintegrins emerged from the snake venom metalloproteinase and are classified according to the sequence size and number of disulfide bonds. Evolutive structure and function diversification of disintegrin family involves a stepwise decrease in the polypeptide chain, loss of cysteine residues, and selectivity. Since the structure elucidation of echistatin, the description of the structural properties of disintegrins has allowed the investigation of the mechanisms involved in integrin-cell-extracellular matrix interaction. This review provides an analysis of the structures of all family groups enabling the description of an expanded classification of the disintegrin family in seven groups. Each group presents a particular disulfide pattern and sequence signatures, facilitating the identification of new disintegrins. The classification was based on the disintegrin-like domain of the human metalloproteinase (ADAM-10). We also present the sequence and structural signatures important for disintegrin-integrin interaction, unveiling the relationship between the structure and function of these proteins.
Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications

Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational effort, to find benchmarks and extract features, to improve the resolution, and reproducibility performances of the imaging data. Currently, no Neutron Imaging combined with learning algorithms was applied on cultural heritage domain, but future applications could help to solve challenges of this research field. Here, a review of pioneering works to exploit the use of Machine Learning and Deep Learning models applied to X-ray imaging and Neutron Imaging data processing is reported, spanning from biomedicine, microbiology, and materials science to give new perspectives on future cultural heritage applications.
Intention of COVID-19 Vaccination in the Sri Lankan Context: A structural equation model approach

Introduction: The intention on vaccination of COVID-19 is influenced by demographic and psychological factors. An understanding of the factors that influence the intention of COVID-19 vaccination is important to achieve the successful vaccination programs. Objective: The objective of this study is to determine the critical factors affecting the intention of COVID-19 vaccination in Sri Lanka. Methodology: An online questionnaire was implemented amongst Sri Lankans to acquire the primary data. The questionnaire assessed the social demographic features, vaccination details, perception and intention about the vaccination, social norm, media exposure, and trust towards vaccines. Descriptive analysis was used to analyse the demographic characters and vaccination details. The structural equation model was used to analyse the relationship between the intention of COVID-19 vaccination and other relevant factors. Results: The results showed that the perception of vaccine, social norms and trust were significantly related with people’s intention towards vaccination, whereas media exposure showed an insignificant relationship with the intention towards vaccination. Meanwhile, perception about vaccine and media exposure had significant relationships between trust of the vaccines. Conclusion: It was conclusive that the intention of the COVID-19 vaccination was greatly influenced by the perception of vaccine, social norms and trust besides media exposure, which had an indirect effect on intention towards vaccination of COVID-19.
The formation of weak metal lines in the atmospheres of O-stars. The role of microturbulence and transonic matter outflow

The influence of velocity fields from radiation driven winds on the formation of weak metal lines in the atmospheres of O-stars is investigated by the use of a simple atmospheric model. It is found that the transonic velocity field has a significant effect on the observable line equivalent widths, in particular for ions of large atomic weights such as iron. Hydrostatic calculations neglecting the velocity field need significantly higher microturbulent velocities to achieve the same equivalent widths. It is shown that effects of the transonic matter outflow on the shape of the profiles of weak metal lines are masked by the usual high rotational velocities of O-stars
Power and energy consumption of skid‐steer rovers turning on loose soil

This study highlights two key phenomena affecting power and energy consumption of skid‐steer rovers on loose soil that is not present on the hard ground: soil excavation due to wheel counterrotation and impeded turning when dragging a braked wheel. Experiments in the field and in a controlled laboratory sandbox show that, on sand, power peaks by 15%–20% in a newly identified range of turns with radii between half the rover width, B ∕ 2 $B\unicode{x02215}2$ , to R ′ ${R}^{^{\prime} }$ , the radius at which the inner wheel does not turn. In this range of turns, the inner wheels rotate backwards but are being dragged forward through piles of sand they excavate by counterrotation. At R ′ $R^{\prime} $ , turns are shown to take much longer, leading to higher total energy consumption over time. Experiments in a controlled laboratory sandbox isolate the high motor torque and the resistance force experienced when a skid‐steer rover drags a counterrotating or braked wheel, respectively, through loose soil. Other field experiments also demonstrate that paths combining circular arcs and lines can lead to energy savings of up to 15% relative to common ones consisting of point turns and lines; the experimental results suggest the circular arcs should have radii of approximately 2 R ′ $2R^{\prime} $ . The quantitative values presented in this paper are specific to the rover and soils tested, but there are reasons to support the overall conclusions generalizing to all skid‐steer rovers in loose soil.
Right time to join? Organizational imprinting and women's careers in public service organizations

Women across the economy continue to encounter difficulties in progressing their careers as occupational segregation and precarious conditions symbolized by the “glass ceiling” and “glass cliff” indicate. Theories of imprint-organization fit indicate that organizational munificence at the time of appointment may be an important influence on individual careers. Prosperous conditions at the time of hire might generate confidence and capability, whilst scarcity may prompt resourcefulness and resilience. While these effects have been examined in small private firms, they have been relatively unexplored in the context of public service organizations. Moreover, there has been little attention to the study of organizational munificence in the context of women's careers. Drawing on the Integrated Database for Labor Market Research in Denmark, this study evaluates the impact of organizational munificence at appointment on subsequent financial career benefits for women in Danish public services. Results indicate that resource scarcity at the time of appointment is related to enhanced career outcomes, and that the influence of conditions at the outset of a woman's career is more pronounced for women with lower levels of experience and education. The theoretical and practical implications of these findings are discussed.
SN 2013ab : A normal type IIP supernova in NGC 5669

We present densely-sampled ultraviolet/optical photometric and low-resolution optical spectroscopic observations of the type IIP supernova 2013ab in the nearby (�24 Mpc) galaxy NGC 5669, from 2 to 190d after explosion. Continuous photometric observations, with the cadence of typically a day to one week, were acquired with the 1-2m class telescopes in the LCOGT network, ARIES telescopes in India and various other telescopes around the globe. The light curve and spectra suggest that the SN is a normal type IIP event with a plateau duration of � 80 days with mid plateau absolute visual magnitude of -16.7, although with a steeper decline during the plateau (0.92 mag 100 d −1 in V band) relative to other archetypal SNe of similar brightness. The velocity profile of SN 2013ab shows striking resemblance with those of SNe 1999em and 2012aw. Following the Rabinak & Waxman (2011) prescription, the initial temperature evolution of the SN emission allows us to estimate the progenitor radius to be � 800 R⊙, indicating that the SN originated from a red supergiant star. The distance to the SN host galaxy is estimated to be 24.3 Mpc from expanding photosphere method (epm). From our observations, we estimate that 0.064 M⊙ of 56 Ni was synthesized in the explosion. General relativistic, radiation hydrodynamical modeling of the SN infers an explosion energy of 0.35 × 10 51 erg, a progenitor mass (at the time of explosion) of � 9 M⊙ and an initial radius of � 600 R⊙.
Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.
Mounding treatments set back bryophyte recovery on linear disturbances in treed peatlands

Treed peatlands are dominated by bryophytes, particularly Sphagnum spp. , which create the characteristic hummock‐hollow microtopography. This, in turn, shapes the distribution of bryophyte communities. Disturbances can lead to a loss of this microtopographic variation, impacting the bryophyte community. Seismic lines are deforested linear disturbances (approximately 3–8 m wide) created during oil and gas exploration and very extensive in Alberta, Canada. Loss of microtopographic variation leads to slow tree recovery on seismic lines in treed peatlands. To facilitate tree establishment, mounding is being applied to restore the microtopography on seismic lines. We compared bryophyte communities on unmounded and mounded seismic lines (18 years after seismic line creation, 3 years after mounding), and in adjacent treed fens as the reference, to evaluate how recovery varies with microtopographic position (i.e., top of hummock, slope or side of the hummock, and level ground). We found that recovery on the unmounded seismic lines was underway 18 years after seismic lines were created, while mounding redisturbed the sites and thus set back recovery, at least in the short term (3 years post‐mounding). In unmounded seismic lines, Sphagnum cover was similar to that of reference treatments at all microtopographic positions. In contrast, mounded seismic lines had lower Sphagnum cover and higher cover of true mosses (sans feather mosses) than reference and unmounded treatments. Overall, these results show mounding sets back the recovery of bryophyte communities—the very organisms responsible for the microtopographic variation we are trying to establish. Further monitoring is needed to understand how this changes over time.
Numerical Solution for Fractional Partial Differential Equations Using Crank-Nicolson Method with Shifted Grünwald Estimate

In this paper, the novel hybrid finite difference type Crank-Nicolson scheme with the aid of shifted Grunwald estimate is proposed to solve fractional partial differential equations. Consistency of the proposed method is confirmed using fractional Taylor’s expansion. Error analysis and properties of the scheme are proved. It is proved that the truncation error for this scheme is of the order of the fractional. Stability and convergence of the proposed method is proved. The exact solution is obtained via two-steps Adomian decomposition method. Companions are made between this proposed scheme and the closed analytical form solution. Numerical results are given.
Moral positive illusion: self–other valuation difference in moral foundation theory

ABSTRACT People tend to be unable to evaluate themselves accurately in many areas. One such area is their own and others’ morality. The current research explores the self–other moral valuation difference in the context of moral foundation theory. We propose that people generally have a moral positive illusion. Specifically, people overestimate their own morality and underestimate the morality of others. Two studies provide converging evidence that individuals underestimate the average moral valuations of others on the five dimensions of moral foundation theory. In particular, we demonstrate three moderators for moral positive illusion: moral foundation type, gender, and political identity. Specifically, compared with the binding foundations, people have greater moral positive illusions based on the individualizing foundations; compared to men, women have greater moral positive illusions; and compared with liberals, conservatives have greater moral positive illusions based on the binding foundations.
Image segmentation using MRFs and statistical shape modeling

In this thesis, we introduce a new statistical shape model and use it for knowledge-based image segmentation. The model is represented by a Markov Random Field (MRF). The vertices of the graph correspond to landmarks lying on the shape boundary, whereas the edges of the graph encode the dependencies between the landmarks. The MRF structure is determined from a training set of shapes using manifold learning and unsupervised clustering techniques. The inter-point constraints are enforced using the learnedprobability distribution function of the normalized chord lengths.This model is used as a basis for knowledge-based segmentation. We adopt two approaches to incorporate the data support: one is based on landmark correspondences and the other one uses image region information. In the first case, correspondences between the model and the image are obtained through detectors and the optimal configuration is achieved through combination of detector responses and prior knowledge. The second approach consists of minimizing an energy that discriminates the object from the background while accounting for the shape prior. A Voronoi decomposition is used to express this objective function in a distributed manner using the landmarks of the model. Both algorithms are optimized using state-of-the art eficient optimization methods. We validate our approach on various 2D and 3D datasets of images, for computer vision applications as well as medical image analysis.